pcl_common库包含大多数PCL库使用的公共数据结构和方法。核心数据结构包括PointCloud类和许多用于表示点、表面法线、RGB颜色值、特征描述符等的点类型。它还包含许多用于计算距离/范数、均值和协方差、角度转换、几何变换，等等。这个模块是不依赖其他模块的，所以是可以单独编译成功，单独编译出来可利用其中的数据结构自行开发，当然想单独提取出来编译时需要自行修改cmakeLists的，这里就不再赘述。
那么我们就按顺序来解释其中每个函数的作用，有必要的话，我会解释其理论并结合代码实践。
PCL_common的类：
（1） class   pcl::BivariatePolynomialT< real >
这表示一个二元多项式，并为它提供了一些功能接口。
 
（2）class   pcl::CentroidPoint< PointT >
一个泛型类，它计算给输入点云的质心。
这里我们用“重心”不仅表示3D点坐标的平均值，而且表示其他数据字段中的值的平均值。通用的computeNDCentroid()函数也实现了这种功能，但它是以“不智能”的方式实现的，也就是说，不管字段内数据的语义如何，它都只是对值进行平均。在某些情况下（例如，对于x，y，z，强度场），这种行为是合理的，但是在其他情况下（例如，rgb，rgba，rgbl(label带标签的)），这并不会导致有意义的结果。
 
这个类能够以一种“智能”的方式计算质心，即考虑字段内数据的含义。目前支持以下字段：
* XYZ (x, y, z)  计算每个字段的平均值
* Normal (normal_x, normal_y, normal_z)    对每个字段平均值，并将得到的归一化的向量。
* Curvature (curvature)    曲率的平均值
* RGB/RGBA (rgb or rgba)   rgba每个通道的平均值
* Intensity (intensity)       强度平均值
* Label (label)                  标签字段的平均值
举例子

CentroidPoint<pcl::PointXYZ> centroid;

centroid.add (pcl::PointXYZ (, , );

centroid.add (pcl::PointXYZ (, , );    //这里是在centroid点集中加两个点

pcl::PointXYZ c1;

centroid.get (c1);   //直接使用get函数获取该点集的在每个字段的均值

// 得到的结果是: c1.x == 3, c1.y == 4, c1.z == 5

// 我们也可以申明一个不一样字段的点云来存储结果

pcl::PointXYZRGB c2;

centroid.get (c2);

// 其中x,y,z字段的结果还是: c2.x == 3, c2.y == 4, c2.z == 5,

// 但是 c2.rgb 是不被触及的
（3）struct   pcl::NdConcatenateFunctor< PointInT, PointOutT >
点云点集相加的辅助函数
在这里要特别申明一下点云库中点云的相加有两种方式：
  • 比如：cloud_c  = cloud_a;
       cloud_c += cloud_b;//把cloud_a和cloud_b连接一起创建cloud_c  后输出
输出如下图：
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asTnT6dNMHu3/sEy/3nxcf/uJA4Ii4PydQriuo3EH+IjPXwxMjc4A58LFxgEWcBZ8FnznAHGAOMAeClAMs4kFquI9ttcn95R0Wc4A5wByYyQEWcRZxXoEzB5gDzAHmQJBygEU8SA3HK1LelTAHmAPMAeYAiziLOK/AmQPMAeYAcyBIOcAiHqSG4xU4r8CZA8wB5gBzgEWcRZxX4MwB5gBzgDkQpBwILhEPj0VWbSvumwNWTGHkYRt25boJCOPVGJFIKK1De+8gRYiTgpRIwV7asCfPOeoURRLTZGBTSzcGJ6QybzD8oB21Oqcy87Q4PeohIMxwM7IiXVeISijTd+M6ZZMy/X4Ayc7JVIT7RVmjUktQ29KJe4MGGCfuoNo1op0/iCjcHtFVsIe+C2EoZi/LjoQiii3V4xzFVR8+rUW4KxbC/fIDzsLv8oZhBOat/AonbjzGqDnj3iRe9F3EvqLEaXHKZblK2cZE6rFgKNN32X758i5R/nA53nEzBzxxIIhEXIXPq6XQrAbcatLji3V6NHRT3OaJLmz2SchUiNtwDKfqtqFkTREKKvbg9K9vYHr9E4pjlZbVaHg8is+PwjTZg4ZKHTK0Zfj60hCFmOzBjhRrhKrwKCzMykXWaq39WbX+O/zyhjKenS2xx4C3Aa9I3UHZ0F5jZIaIC/YrTIPkbR0YnBzEzdP7oS8rQnbWMkSprW32h3ib6xBsjw/v89h3IQwF7EVt+TRuJSoOX6aUtpaF1YsZIi7YL7/gLPguOQxVqai5M4TfOuqwaf0XKNhQi/97TAvYyRuoSLDGKhfhqkg9Ultk+y7QL9F3yfWd/513xswBIQ4Ej4jbEj1cqXAkSYnfiCtuk6T4tmqLWHOZFgdPcCjdItAhVG8X7Xx+/TYVn1qJFLKgGO0kwIPHsh2hKp1JRuFgSzpI+AdPICd6urCGxhThRL8BvQcLUPQjpQp13okL9YtSQOYcw5PxbuzKmD8tN7rfV+hC7RHH12vfZ+yUPWPo3E9Xe81RRCK1vg9DD1rxbXkFTtFOfIaIC/XLTzgLvUsMwxBlpCN9KOEVntuCF6ZRnNVZYp6LclWuHsmLIcsxwX7Jv0us737nNouCkCgw7sHFz6ARcUfKxSQs2nkT4y97UJuWbElX2jvbdKXkOlxSiD03xvHXkyPI1FiMF7poG+5K+Zc3O8VmD1+MLXfpXT1fIso5/7LVjavSNeO5aQTn1k53y89RLkT5xSEMd1QgVmnNQ+0k4kL9ojjk669TPHjDI/QNjmGK3Psvn/2M5m3ZUElx3P04OQm1R/R9Mn2f3m4lPGJof597e5nrCVNaFjcesqEJ9ctPOAu9SxTDaeUiEF1xjRac91GTop4lVy0uc9d6pFj3chybXb/cvGtWffcvz/05Zrguts3fyYGAEvFQdTTUC2IQKT3R0VA4CZQis4lcpeNozY9H+tF+c9axU6spqUPHS8pwVY9Ut/m+PZMrNP5L3DKfM9Iz0YN9K6MdO1zacdQ/pb8/aoRuYST9ncQjqdCSTau3GrGuwqlMRvUdcsnfr8WiCOd3qhCvv4Kx52dRYE7VOFPEhfqlKUAreQHGrh7CxoJVSM3Uoqyh25z17XyZU25wwcnx/eAs3/dpxPeIoXVh5c1ezv32IOLvE2ehdwnayhkjRUo1LlGCoD++10FtW0j6ylV6r9t6BDg2m365fdcs+v53TpL8bhbpQOZAAIm4VeDsmbGG0ZxrTZMoTTx2Eae/KWMQnxSLzxRRsxbxOeHRSEhfhdXrv0Jj1zBMU3exL9P2PnItZtXi4jNLxifLM2H+72Rn8Yz8y4r0evxheoVLZdN34aFxZWgfHcGF8gR7es5V/5nuThfpV2j8JtyWPANbFjlcq6rl2EuLCuONDdDM8Ax4G3TvB2eRvk8TKA8Y2st4tZdTf2VF3DN//IWziE19mxSIjyt2050Kum9xXo+FzpciJTe4MFc91yPSd9/65a3NLAq+2Z/xYrw8cyCARDwCqqVa5OYXQmt+tEiKsl7ekVzcSdvNLu7uKqedZ3iixZ1+d7budCswGh2aKSf2+DmdJY2m/VFDnZCChUmJ0Cz7Bg/p/b9sXzLtZvAcxTykfU/5yA2t0EU5/1aJ+RW3nBYBLjfZp3qgXxQh1K+Q2ArckvJn719OCxfrO8LiUNJNdT6oQaLSl0H+PnAW67sDZ08YeuiXR3uJuNM988dfOPuXqxFQ5xxAD9396D+zAfEePU5yXPVej0jfxfsl2mZfeMtlWciYA+44EEAiLmMg26Wazg2Oi21xG9Hp9mKbClF5Nag7vAvaeJX8mbFGi+ZhIwztBS4ibmlTiHoZqq/TTXhDB9aa3eJObZXcmc+MeH1hDdQubsLQ+YuxOHU5ltifbFReonoGT2JN+hKoJfEV6ReVOTxAu+7bWxBnc+XTjrOR/vaK3qvyp3tSpD3293nGWajvtnq8YOh24vJmLw878feKs58wlPo+N7EKF8aNeHamDAsEFmueuCpbjwjHBPsl+y5/8pXrkp/fGKMPGqPgEXGFGglbpU/MxtFVvxmFazej7n/jdJ59E3rXT8wiV+PkkGXnO9ySizBnEisTUbD3ILZXlUCXX4C80moc6qSdtOlPtOTHWM/F6XvjxHSsyFuHqm9O4OpTcqsb76OxwOXSmjTJJu3EPXpPj97pEpzHQTPzTHyOUL8ikbSnF3/R53XXD5QiK0uHypMPYTT1oynHceTgn5W6n3CegYG7vlsWQ14xFLIXLbRUCUhKy8DS7Er8SF4Vw0U9MtIzsCRxvvUIQqRf/sJZ5F3WhaA3rtIdiqxTz2nx+BM25azAUuqP7UlZEmP1CIlwVaQekb6L9EvkXbyj8s9YZRwZx3AEkYgTYcPjkL2nDQ/pbFAS6LG+duzWugn2Qp97FZ95giljP86UuoirKgVVP3Th0RDd9jafdRsw0Hse+9cnQ2E/W56H9OPPzIFgXvx+G+1Ht2N1ovX7cBdxUv7zLMakS3Y5GoHVngchE+mXki7x7b+APnOgGyMmnnWhsZza/C5W2SLtkd7rDWcfRNwrhkL2ojsTWUcwYL+/4Di6MLTmObwrIv3yF84i75LDMCIFO/s8BBT6RY9os1dGgKtC9VBdIn2X65fou94Fb7lOgTmIhf9DE/7gEnEepDxImQPMAeYAc4A5YOcAiziTgScE5gBzgDnAHAhSDrCIB6nhPjSXEPeH3ZzMAeYAc8B3DrCIs4jzCpw5wBxgDjAHgpQDLOJBajhesfq+YmXMGDPmAHPgQ+MAiziLOK/AmQPMAeYAcyBIOcAiHqSG+9BWk9wf3iExB5gDzAHfOcAiziLOK3DmAHOAOcAcCFIOBJeIh8ciq7YV980BT6Yw8rANu3LdBHvxZox5Wpwe9RBAY7gZWZHSSigSCaV1aO8dpAhxUtnXeH6vDXvyZkZsk/Iwhy3V4xzlsB4+rUW4y7tDIpei9Og1PKYsZOYgLQPd+GFrJiKcMqGFaDKwqaUbgxNSmTcYftCOWt30d4nUI7SK9QeG9j5SdrfUEtS2dOLeoAHGiTuodo2eJ2WA81RGyBa2lal3nOeI9EudjgN/zLR998Z433K0i7zLpwlBCWX6blyXOOKca35aHXJlvOEjyGef+iXXHt93FEL89QlXbgNj+uFzIIhEXIXPq6WwqwbcatLji3V6NHRTHPKJLmyeIRxeDBcehYVZucharbU/q9Z/h1/eUIaosyXWuOwqxG04hlN121CypggFFXtw+ldKNfr6JxTHKu0r1k/jVqLi8GVKkWoRhheuIh4WjZyWP6mNt9FQqUNaVj6qjvVi0vQC/ymyhngNj0fx+VGYJnvMZTK0Zfj60hCFee3BjhRrlDiReoQmNz9hKL0rTIPkbR0YnBzEzdP7oS8rQnbWMkSpHfjIlhGyRThkcVYI9isyB8cpHO/jhmIst4cwTUeCU6Id+UlP8F1C9rDwVJG6gzKUvcaIFxH3VkYEH3k++9YvkTbLY/nhT7CMAdv4XXMgeETclnzhSoUjAUr8RlxxmwDFB+JQ6NCSDhLRwRPIiXYSIJdJOGLNZVpAPMGhdFv41Uik1vdh6EErvi2vwCnaic8QcfVKHKG/DzU7xW+3xsp+2phpDpkaQn3oorzmv36bik+t7wxZUIx2mtAHj2VbwqoK1CNEFL9hSGkmc47hyXg3dmXM97CLFSnjYie3thDAWbRfmkK0v5pEV2ns7F2Hou8SFPHQmCKc6Deg92ABin6cnqbWZlPvZQTwcdOWGXz2oV8ibRbioyBGXJcP8xljOvuxHaTYBY2IO9IgJmHRzpsYf9mD2rRkSyrS3tmmIlVCpWvGc9MIzq115yqXBg+5g5cUYs+Ncfz15AgyNU4DKkxpETBPmbPCF0PfQ67//qPIWWDJfqZI24sHxlG0rY0z/zZ00TZzitU7m51ivNPvttylfvV8iSgpnrtAPSITnd8wDP8c669T7HnDI/QNjmGKjgBePvsZzduyobIdE4iUcXEXe7SFDM6i/QqJr8LPRgMuVaUhOjrSNxe6ta2i7xKxxxzlQpRfHMJwRwVilR7i6ouUkePhNJzd81m4XyLtCdLJUMhm3LePTiQDnRcBJeKh6mioF8QgUnqio6FwOjdWZDaR23ocrfnxSD/aT+5rSjqympKCdLyE6Wk9Uj3mWfayilUmo/oOucnv12JRxMxyofFf4hbtks2JUiZ6sG9ltPuJ35OIS6K9tBpttBs3jd1Dx5l23Hv5EnfqddDY+ial4XxK//6oEbqFkrDQJJtUiLrfpcVJNWKt5WTrEZhc/IahpgCt5CkYu3oIGwtWITVTi7KGbnOGufNl1nzdImWc2yxjC/NA8oCzaL8+S6nBtYERvLLadGrgJo5WpiLMnvhGfscj+i75ga9CvP4Kxp6fRYE5va07ERcp49RmLzyU2uONz2L98rE9ApyUx0neJlwHY/QxcyCARNw6idkzUQ2jOdeRZtMxydDflDGIT4rFZ4qotxJxRXo9/jC9wqUyD7vw8GgkpK/C6vVfobFrGKapu9iX6Sb1p8fJMwKa/Cb0jg/gcn0NdjW1olfKwPb0HDYutrnlye2cVYuLzyzZySzPhPm/k53F1lzhIvXID2R/YRgavwm3Je/BlkXWNJ/0btVy7KWFh/HGBmhIFEXKOA88WVsIibgoNyKgTNJi0+lHlN51CC0FthS07xHDuDK0j47gQnmCFcOZIh4qUGba5CUj4nO88FmEGz63h0Wcd63MgXfOgQAS8QiolmqRm18IrfnRIsnpwpHD3Wfd6UnkCE+0uNPvzsadPg9p31MecUMrdFHyk/ccjQ7NlKd6/JzOkdrSRlBPk6cmD6dH6Lz732n2lKGhCwpwnHbmry+uQ+S0HaAa6oQULExKhGbZN3hIIvnL9iWWnNE+1eO5L/7CMCS2AreofY/3L6eFlPV9YXEo6SZbPKhBopLO+gXKOARI0BYecJ51v+blmvPOG9ryZ9rUw+Qz63e5HB3Mr7jltGhzuTE/1QP9IhXky1iOaOyPnIg7l3Xhs3y/lL63hyfwdz6Bf8w7UO67ZewHkIjLCKnt4k3nBsfFtriN6HR7sU2FqLwa1B3eBW28yv1AktzYz0hML6yBWmSy0WjRPEwTfnuBsIjPTdqOe/R52uV1MU5tiEHxtUly4e/A5yR2rkQMUS9D9XW6dW/owFqzmzUcs6nHLcH9hSHVc3iAdt23tyDOdixAAtJIf3tFeKokPEXK2HAXtYUnkfKpX06Yk01P+SjiUr8aiDcTb8nD0PmLsTh1OZbYn2xUXiK7D57EmvQlUBM3RMrMXsRd+CzQL5/bIzKuuAwLPXPgrTgQPCKuUCNhq/SJ2Ti66jejcO1m1P1vnM6qb0Lv+omZ9Qa45JIebnG6Ge5ElrlJO0lgjejRO10os/27MhEFew9ie1UJdPkFyCutxqFO2rWb/kRLvsP1GqJKQFJaBpZmV+JH2qUbLuqRQZ8uLUmcb3GRqlZYzraf/hfb16xEyvJs5NecQ7+0i63LsO7O6fvexHSsyFuHqm9O4OpTcqsb76OxwMnFL1SPgDfBbxhGImlPL7miDbh+oBRZWTpUnnwIo6kfTTm24waRMpY2e7UF/bsszkL9mo/0XfX4eks58smmutKtqLsi2dQ3d/ocoXdZbSHAQ4cIe7jYNm2Cc19GFh8hPvswvuxtEmmzCC+5DO8qmQOz5UAQibjkPo9D9p42PJTOlUkIx/rasVvrJtgLfapUfOYJpoz9OFPqRqRpElL+8yzGpMtxOZqZqyBVCqp+6MKjIbqBbT6jNmCg9zz2r0+GwskFrsg6ggH7ObbDJWpozbPv1hXJG9Bw9TcYrOWMo4/Q2VSORLWNtPOQfvyZOaDMi99vo/3odqxOtJ2XO4gtX4/gIPAXhkq6VLj/AvrMgXdoZ/qsC43lhM+0y2oCZeRsQf8ugrMsN+i2fN7hTvQNvbLaVArgcwEHS1Om2VRoIPkLQwGBnt4e96Ipi48gn2UxnLFjYhEX4gvvNN9qp8kYe5/bg0vEeTDwYGAOMAeYA8wB5oCdAyziTAaeEJgDzAHmAHMgSDnAIh6khmMXk+DxAduXJ2fmAHPgA+YAi/gHbFwWehZ65gBzgDnwYXOARZxFnFfpzAHmAHOAORCkHGARD1LD8er6w15ds33ZvswB5oAIB1jEWcR5Bc4cYA4wB5gDQcoBFvEgNZzICo3L8EqeOcAcYA582BwILhEPj0VWbSvumwOMTGHkYRt25boJ9uJNmOdpcXrUJVa1LWDLcDOyIiWDRyKhtA7tvYMUIU4qKwUGacOePE/pSpVQpu/GdcrsZfr9AJJtgVyE3mUjGGUvSy1BbUsn7g0aYJy4g2rXSHRShjPZMjKE9QeGhG9I5FKUHr2Gx1KfpWAvA934YWsmIpwyz81Rp+PAHzOx7t4Y7yYbnAcMJVsK1CPUHjMvKELeUj3OUfz64dNahL+rRZyfcJ4jW48vXBXhjxd8hPgs0h6RMh/2xMvCyvb1FweCSMRV+LxaCrtqwK0mPb5Yp0dDN8WanujC5hli54Ug4VFYmJWLrNVa+7Nq/Xf45Y0RQ2dLrHHZVYjbcAyn6rahZE0RCir24PSvlLL09U8ojlXOcDspUnfg6thrjLiKuNC7qK1hGiRv68Dg5CBunt4PfVkRsrOWIUrt9C6RMrKC5CcMw6KR0/InYX8bDZU6pGXlo+pYLyZNL/CfIqeMYJE5OE6xyR83FGM5haNdan7SkeCU2MZGZI8YSn2Sq0ewPZ/GrUTF4cuU0taysHjxzkTcTzgrROoR5KoAf2TxEeKzSHtEyvAk769Jnuv5sLkUPCJuS9BwpcKRACV+I664TYDig9EoRGtJxyglnjiBnOiZAm0bABFrLtMC4gkOpU8PiRoaU4QT/Qb0HixA0Y8j03firqLq9l2UijTnGJ6Md2NXxnz3+cpp9yhfRqDP/sJQvRJHaCc71OwUl94aJ/xpY6Yj9KqmEO2vJtFVGuv1vE0WQ7l6hNoTidT6Pgw9aMW35RU4Re1/ZyLuL5xnWc9MrorwZxb4vOXYcRYXT+OLBUhgXMsu3rmOD5lHQSPijlSJSVi08ybGX/agNi3Zkoq0dzapSC2uVZWuGc9NIzi31pOrnFyQSwqx58Y4/npyBJkapwGhXIjyi0MY7qhArFIujrSHd1FM7/XXKUa74RH6BscwZXqDl89+RvO2bKhsrmmRMgID2W8Yhi+GvoeONPqPImeBJdOaIm0vHhhH0bY2zr4QCYmvws9GAy5VpSE6OtL9AkUAQ9l6BNszJ0xpaYMvKTsFcHWdIPyFs+/1eOCqKH98wuctx44dVy/jaxbYf8iTNfeNFyPuOBBQIh6qjoZ6QQwipSc6Ggqn81VFZhO5QcfRmh+P9KP95uxTp1ZTgo2Ol5QlrB6pqlkYWJmM6jvkJr9fi0URM38fGv8lbhmtZ7oTPdi3MtpJiFSI11/B2POzKDCnDJURcU/v0hSgldzwY1cPYWPBKqRmalHW0G3O1na+zJo7XaSMwITnTwwVS6vRRrtZ09g9dJxpx72XL3GnXgeNk80+S6nBtYERvLJiODVwE0crUxFmTyIjhqF8PbSIEGiPfQD4QcTfB1d9sZdXrvrKHxF83mrsWMaa9/E1i/EsMAZYCBnXD40DASTiVhG0ZwUbRnOuLa0lTdJ2Eae/KWMQnxSLzxRRbyXiivR6/GF6hUtlHnbh4dFISF+F1eu/QmPXMExTd7Ev09Km0LgytI+O4EJ5giXtqIyIe3pXaPwm3KY+39myyFoP1aVajr2UwtR4YwM0JHgiZUSI6T8MI6DJb0Lv+AAu19dgV1MreqXMck/PYePimRnY5tCFPGWSFptOP6L0pY7Un75i6Kke6e8+tUdEpLwKwvvhqk/28sZVAY5N448APm8zduzv8tJmET5zGRZk5kA4AkjEI6BaqkVufiG05keLJKcLUA7XonV3Kk2y4YkWd/rd2bjT5yHte8onbWiFLkpgMGh0aKac4ePndJRmVIn5FbesKS3d3HSf6oF+kcXNbHk8vysktgK3pPzi+5fTosRaPiwOJd1U74MaJCrpJrhAGREy+w1DTR5Ojxjx67/T7OffoQsKcJx25q8vrkOkU7rWae2al4uTdNHN0JY/CwydbDStHvq7r+0RECnveL4frs7aXtO4Ogv+yOLzNmPHw1hzabMIn7mMwLzF3okP/vv3ABJxGULaLvl0bnBcbIvbiE63F9tUiMqrQd3hXdDGq9wbkeqrf0aic2EN1CJE12jRPEwC1F5gzhUeOn8xFqcuxxL7k43KS3RbfvAk1qQvgZrE1z7JeHsX/dvhAdp1396COJsrmibRRvrbK2qbSmqbSBmRPvgJw7lJ23GPPru7vC7GCdsYFF+bpKOJHfjcue/O7SIMT9lF3EcMvdTjc3tkReotJ0c/4SzZvYE4OiHEeac2u3DVZ/7I4fOWY8et+Lq2WYTPXOaDFyheqMnPRcEj4go1ErZKn5iNo6t+MwrXbkbd/8bpM6eb0Lt+Yma9KS19RjTc4nSD2mnQz03aSUJkRI8+0eHGtv27MhEFew9ie1UJdPkFyCutxqFO2rWb/kRLvtMnVNMmEc9n4l7fRd+kJ+3pJTezAdcPlCIrS4fKkw9hNPWjKcd2nCBSRt7Yc/yFoWoF6sjdb3r6X2xfsxIpy7ORX3MO/ZJHoS7Dujufj/Rd9fh6SznyCUNd6VbUXZEwdLjTZw5QdxgK1CPUHtqRqhKQlEafuWVX4kfyqhgu6pFBn70tSZw/kwNvJRD+4qpAPUJcFeOPKD5vPXaE2izCZy7DIsccCCIRl9znccje04aH0vkrCcZYXzt2a90Ee6FPX4rPPMGUsR9nSt2INE3Qyn+exZh0OS5HM3M1q0pB1Q9deDREt8bNZ/QGDPSex/71yVB4chV7ORP3+i5JLJR0QW//BfSZg9jQzutZFxrL6V3OQiJSRkR4/IShInkDGq7+BoP1DoNx9BE6m8qRaAt0Qzei8w53om/olRVDKWDOBRwsTfENQ8F6ZNsj3aDPOoIB+50LxzGIoTXP7F3x64ToJ5xlOS/KVQH+iOLz1mNHtM3+tgnX51+OM54BgWdwiTiTJiBI41exY5uyTZkDzAHmwKw5wCLO5Jk1eVjM/bx7Zy4yF5kDzAEfOcAi7iNgLFwsXMwB5gBzgDkQKBxgEWcR55Uvc4A5wBxgDgQpB1jEg9RwgbIK5HbwjoQ5wBxgDvx9HGARZxHnFThzgDnAHGAOBCkHWMSD1HC88v37Vr6MPWPPHGAOBAoHWMRZxHkFzhxgDjAHmANByoHgEvHwWGTVtuK+OSjKFEYetmFXrptgL8LGUEKZvhvXKYuY6fcDSLYFKrH/ntIkppagtqUT9wYNME7cQbVzdDi59szT4vSom9jqUsCR4WZkRfp5NSvXHiFcIjBv5Vc4ceMxRs3Zxybxou8i9hUlYq7t9yL9EikzrT1vZ4uQyKUoPXoNjyVbSgFzBrrxw9ZMRDhlVfPbytkvOFMEOU0GNrV0Y3BCavMbDD9oR63OXTIeJcKW6nGOYtMPn9Yi3K0dvXDVJ1t447wAN4Q45mfe8ztZgD9iDgSRiKvwebUUdtWAW016fLFOj4ZuilU+0YXNrmFXBQ2qSN2Bq2OvMeJOxMM0SN7WgcHJQdw8vR/6siJkZy1DlFppHTAC7QmPwsKsXGSt1tqfVeu/wy9vjBg6W2KPAe8fcRFojwguqlTU3BnCbx112LT+CxRsqMX/PaZF0+QNVCRYk7qI9EukjFN73soWYdHIafmTuHAbDZU6pGXlo+pYLyZNL/CfIk9hcmcrJH7COTwexedHCdcec5sztGX4+tIQTMYe7EhxZIL7NG4lKg5fpjS8lsXJC3ciLsdVUVvI1SPCDRGOcRkWXeaA3zgQPCJuSwZxpcKRACV+I664TYAiP0GHxhThRL8BvQcLUPTjiMtOXImInGN4Mt6NXRnznXKIT09qYk5O4Ut7KBxsSQdN3IMnkBNtWwzIt1VI5P2IT4gyclos8fDcFrwwjeKszpEadlqbRPrlpcxb20K9EkdolzrU7BQn3xo//2lj5vTwtW87efgJ5xDibhd5On79NhWfWtsUsqAY7bSgHDyWbW1zJFLr+zD0oBXfllfgFPVxpogLcNW1z25tIVaPz9x4W7z5936b7IXmEcY76PAOGhF3pGVMwqKdNzH+sge1acmWVKS9PqYiVS5E+cUhDHdUIFbpJukGxetef53iphseoW9wDFPk6nz57Gc0b8uGyuqe9b09Sqh0zXhuGsG5tR7yl7/FAPK9PaKLhwhEV1wjD8h91KSo3RBcpF9eyvjBFnPCF0PfQ96C/qPIWWDxFijS9uKBcRRta+PcL8JmibW/cA5dtA13pTzym51i+1M/ttwlPvd8iShbjP4wpaX9njKLCXB1+uTtwRY+1yPxR44bohzjciywzIHZciCgRDxUHQ31ghhESk90NBRO55mKzCZyKY6jNT8e6Uf7zdmwTq2mxCEdLymbVj1SVaIkUCFefwVjz8+iIE6a8N2IuKYArbQjGrt6CBsLViE1U4uyhm5zBrXzZZZ85j63R5mM6jtvKFVnLRZFiLZ1ern3g8/0dypSqnGJEs788b0OanfJX0T65bGMf2xhFu2l1Wijnapp7B46zrTj3suXuFOvg2YWZ+LvBWcpnedTau+jRugWRpJQ01l0UqElO1xvNWJd2+1JxAW4Om1y8GQLX+uRMJfjxiwXSrOdzPh3s5tXGLfgxi2ARNwqpvYsU8NoznW4bx2iSX9TxiA+KRafKaJ8FvHQuDK0j47gQnmC1WU8U8RD4zfhtrRL2rLI4VZWLcdemmCNNzZAQ2Lma3sU6fX4w/QKl8pmuwt/P/g4BjS5V1fspjsD5KY+r8fCGZf+LMQX6ZenMv6yhbQj1OQ3oXd8AJfra7CrqRW9Uqa7p+ewcbHjfFlssnpfOBO+WbW4+MySuc7yTJj/O9lZbMkj7/x4EHERrjrX49EWApz3lRtieAf3BMp9ZPv93RwIIBGPgGqpFrn5hdCaHy2SoqwXqWgyc7gxLTthM3DhiRZ3+l1Rd7oS8ytuOU2aLjfHp3qgXxSBkNgK3JJyY+9fTgsF67vC4lDSTeUf1CBR6Wt75iHte8qlbWiFLmq2pH8f+NjaFgF1zgH00H2D/jMbEO/RyyHSL09l/GeLOZo8nB6h8+V/p9nPv0MXFOA47cxfX1yHSI/pY93Z4n3iLL1fDXVCChYmJUKz7Bs8JN79sn2J40sAG/88iLgIVx2TjGd7idcjyo3Z8px/93eLAr8/uDgYQCIuA5ztQlHnBsfFtriN6HR7sU2FqLwa1B3eBW28atqOJnT+YixOXY4l9icblZfolvvgSaxJXwI1CfQcetfhAdp1396COJtbkybRRvrbqwtrLLskX9ojuU7pEtxr+q36XbkYfWmPwjM+0gCem1iFC+NGPDtThgUSHp7aLNIvL2X8ZYu5Sdtxz/Qal9fFOLU1BsXXJun4Ygc+99YHX+3hR5ydcQ1RL0P1deKhoQNrzcc8YjtxIa7a6vJmLxHO+8INX3Hl8kF3oYrFPjDEPnhEnHYsCVulT8zG0VW/GYVrN6Puf+P0WdFN6F0/MbPeTJZck8MtTjeW3U4Ubs7EFZFI2tOLv+hztusHSpGVpUPlyYcwmvrRlGNz8Yu3Z27SThIZI3r0TpeY/D5pibdnjld85iPr1HMSk5+wKWcFlqZn2J+UJTHTdogi/RIp45gMZmkL1QrLWfLT/2L7mpVIWZ6N/Jpz6Je8KXUZ/r2d7jce0rffielYkbcOVd+cwNWn5FY33kdjwfTjlhBVApLSyAbZlfjxhRGGi3pkkE2WJM63eqREuGqZbLzbQqQecW7wBB8YEzzb4cO3QxCJuOQ+j0P2njY8lM47aYIe62vHbq2bYC/0CU3xmSeYMvbjTKmccLoTDnqXki7N7b+APnNgGfqU7FkXGsuTpwuCYHuU/zyLMekiXo7m3a62Bdszxxs+ESnY2echQM0vekQ7XbgS6ZdIGe8iLmYLRfIGNFz9DQbr+bJx9BE6m8qR6OEs/60mN3/grJiH9OPPiFuv8eL322g/uh2rE2ee3yuyjmDAfmbusIuhNQ9K20JQhKtUVtYWcvX4wI23wtfvC9wPfyJnvD9eGweXiPPgfreLAMaX8WUOMAeYA0HFARZxJmxQEZZ3HB/vjoNtz7ZnDszkAIs4iziLOHOAOcAcYA4EKQdYxIPUcLwi5V0Jc4A5wBxgDrCIs4jzCpw5wBxgDjAHgpQDLOJBajhegfMKnDnAHGAOMAcCV8TV/8DG745THmunCG0suLxaZg4wB5gDzAHmgJ0DHkU8NKEad5y/T/1f6buLNubOIFEl6KL3P9ydYk/VyKtOXnUyB5gDzAHmAHPAwYFP9rX/gj8pdKmUfGHk95s4uX0VIqWAHpRkZOFyihSVWWyJhhUEIh4aX4a2QUohalt8GMfw9Jf/4tuihbwQ4JUr716YA8wB5sAHx4FPTP0XcGBLKfKKy7Fp3yn856AWEc6GVi7G1vtBIuJW78GDb3VIzViJlcXVaLxN8aj/+gXb3ebC5hUtr2iZA8wB5gBzIHg58Emhu2QLvoi4KgmFB3/Co1FpNz+Fkb6L+HdxkiP7lzoDB/sp5rNzmEg3rvIQTRqqfriFQatX4PmDB+YQmr64021HAN0b4yk/s8UoirRDeGJ6ifMFUdNXYD6FZg1eA/PgZNsxB5gDzIEPlwOf2FNtenKzeNuJh8VAe0qK//wHWr/ZiILiSnzd0U///xlO5sdYhFRExMM/x9rzI5QA4lecqa1AQcmX2HnyZ7x6GxEPU0GZsBKVp5/A+KIVX7guVnxKkvLhEoAHN9uWOcAcYA4ELwfkb6d7EfHQRV+ZL7/9tt+Rx3mOOh37pDP0nmpLGk8BEZ+7pMac5etB7T8cZ9ezuNg24zKe+Wy8Hy1li9yciasQnV+D79ykK2VCBy+h2XZsO+YAc+Bj4sBbiXhEfhtemv7E8VXO2bk0yDj5J6WybEWuRkzEI/LP0677T3y/cp7D5f0WIn7/GzoTT8/EivxSVH8v7eiH0F65aFoazY/JyNxXntSYA8wB5sCHyYFPPpW7rehlJ24R8ec44SLiK6aJeDr2PzHiVUeh48Kci0C7rectRNz5THxOeCIqfqZ0og9qkKD8MI3Ig5PtyhxgDjAHPk4OfKJdEOH9yj2JYFUvuaV7/4UFTrmkJcLY3OmP96c78my7utPDF2Gz9HsS0USbiLoIdOiiavSQ6/tJfaajHr+J+EJU9dD7729H/LT2qxCVV4M6dqd/cJ9c8GT2cU5mbHe2+8fIgU+Mv7fj3/8qha5wLdZvO4Tv6wqhmSZ2GmQ1D9Glswc4+q8SFJX/C1srl1nEli625f5gvdi2txJF677EtxeeTr/YplBjYc0d/GUaR1eDHoX5BcirbMJj50trUj2npN8N41pjNYqLipDvWkbOYyAtKqyfmN3/WovF/0jDstVrUX3yDiZMk+je4RI0hi+2sXgLcOpjnBS4zyyGzIHg4cAnR6//ihGjdAHMiDdD93C+bh1iXNzOcxPWoen2nzBKF8WMQ+g9ucYh9PSJWVFdJ34zWOoY/+0yDqxd7PjETJooqcwXDVfx+0tLGZPpNV48uYXv1zqFVFUtRN7+87g3NGktQ8Fn/riFo8XxCBWcbKVgL+3P3ziCvZhe4XnfVXy/1RrAxrke/sSMRVyQVzyhBc+ExrZiW31sHJC/2MYTHYsdc4A5wBxgDjAHApIDLOJMzIAk5se2mub+8g6SOcAcmA0HWMRZxFnEmQPMAeYAcyBIOcAiHqSGm82KjX/DK33mAHOAOfBhcYBFnEWcV+DMAeYAc4A5EKQcYBEPUsPxavrDWk2zPdmezAHmwGw4wCLOIs4rcOYAc4A5wBwIUg6wiAep4WazYuPf8EqfOcAcYA58WBwIHBEPj0VWbSvuj1Kccykv+cM27MqNEw70Mp2YSoQt1ePcn0YMn9Yi3EWoQyKXovToNTy2Bp+ZGOjGD1szEWGPVBeJhNI6tPcOUrQ3S3Ca5/fasCfPKfDMPC1Om3Oou3mGm5EV6UoUJZTpu3FdeufvB5Csdvx7iCYDm1q6MTgh1fUGww/aUasTD3Jj77ufMBRvTwQiUktQ29KJe4MGGCfuoDpJZV/Ry+McDpEycwT6Jd5mmQEs8C6hSZDCDx/4YyY3psX1N/PSG1cjMG/lVzhx4zFGzQGZJvGi7yL2FSVOT+Yj0maR9oiU4UUv71iZAwHFgQARcRU+r75JgmnArSY9vlinR0O3AaaJLmx2EgWRyfPTuJWoOHwZT63i+sJVxMOikdNCWdYmbqOhUoe0rHxUHevFpOkF/lNkzYGuUCFuwzGcqtuGkjVFKKjYg9O/UiS41z+hOFZpMWB4FBZm5SJrtdb+rFr/HX55Y8TQ2RJEucSZV6TuwNWx1xhxFfHweBSfH4VpssfcngxtGb6+JIW57cGOlEgfyOInDEXbE6ZB8rYODE4O4ubp/dCXFSE7axmi1FZ8RHAWKUO2kOWGaJtlJx+Bd8nWYV0kRObg+JARjxuKsTw9A0vNTzoSohy5CmS5qkpFzZ0h/NZRh03rv0DBhlr832Na5E7eQEWCrR7BNgu0Z45IGdH+czkfxu6HtTMUmae5jP9sHhgirspEwzMjJq5U2MUvNH4jrkwa8fTwCkdSFNmJIRKp9X0YetCKb8srcIp24jNEXL0SR+jvQ825CLPVZ42j/rTRKQGLy7si1lymRcYTHEr3IKwUxrWkg8R48ARyoq1CZq0jNKYIJ/oN6D1YgKIfR6btxEOon120y/r121R7zvOQBcVoJ7EfPJYt3nc/YSjWHiUico7hyXg3dmXMR4g7u4jgLFJGoF9ibRYYNALvEp58NIVofzWJrtJYD5O5AFcJ1xBl5DRvVHhuC16YRnFWN99Sr2ibZdtDdYmUkR2DAjhzHSzwzAG/cSAgRDw0aTvu0s65uyoJi3bexPjLHtSmJaPqjpQ9bSviXHa1XifSMKVFVNTZaHIn4uGLoe+h3Uz/UeRYM7gp0vbigXEUbWvj3AgSuYyXFGLPjXH89eQIMqUc6TMIqIRK14znphGcW+viBlcuRPnFIQx3VCBWOR+r/jNdxEMXbTP3/c7mRMdkTW3ccpf63vMlosLEJkV/YSjUnvDPsf76a8oZ/wh9g2OYoiOAl89+RvO2bKhsthLBWaCMSL+E2iwwaYi8S1TEQ+Kr8LPRgEtVaYiOjnS/0JHj6ow2RyC64hotJu+jJkVt5qFom0XaI1JGtP9cTmzcMk6M09tyICBEXJHZRO7vcbTmxyP9aD+d/Q3h1OoE/LPjJUxP65GqmoWhPYk4TXyKpdVoI4E3jd1Dx5l23Hv5EnfqdS7Z22iCjP8St6zJYUwTPdi3Mtr9ZKxMRvUdcrffr8WiCOe2qhCvv4Kx52dRECe5P2eKuLSTqn9KbXnUCN1CabKnRUNSIep+lxYw1YgVXMD4DUOR9mgK0EqegrGrh7CxYBVSM7Uoa+gmcRnH+TJHUhsRnOXKCPVLpM0CIi70LoF6pEH5WUoNrg2M4JWVP1MDN3G0MhVh7hZlXrjqPMAVKdW4NGbEH9/roLbWI9pmkfaIlHnbCYd/P4u5TJBzjO3HiW2AiTi5CJUxiE+KpSxoUe9IxCOgyW9C7/gALtfXYFdTK3ppYjQ9PYeNi11c5eHRSEhfhdXrv0Jj1zBMU3exL9PqxnQaWIr0evxBGdMulU3fhYfGUVa10RFcKE+w7rLdiDhdbIrIqsXFZ9KFPttFqAnz/57sLIZKcAA7JvO3xVC+PaHxm3Bb8h5sWeTwHqiWYy8tPIw3NkBjFhgRnOXLiPVLvs0iE5zYu3ydKCKgTNJi0+lHlI53CC0FtnsXTvXIijj1b8VuulNBx0Dn9VjodCnS9zYLtIdsJ9tmQV6K4M5lfOUUl2fOODgQECLucAk6pSYNT7S40+/66E63TS6eJkZNHk6P0Bn0v9Ps582hCwpwnHbmry+uQ6Qn97VGh+YXlGr1nA7KaRPYPKR9P0iu5VboopwHlxLzK265v70uifVUD/SLHJec5lDedXVCChYmJUKz7Bs8pDK/bF8y/Rayl4nT/xh6bk9IbAVuUfse71/uSDkbFoeSburXgxokSqlsRXAWKONbvwINQyc+zMvFSbroZmjLd+GPl6Mfs70joM45gB66H9J/ZgPiXbxSvuEj2B4bz7y1mUXcb2eaLEi8KHkbDgSEiNsv53RucFxsi9uITrcX21SIyqtB3eFd0MY7PmeaAYIHEZ9L5+/36JOxy+tinAZhDIqvUR7z+zvwuUsudXu9Gi2ah2kSbi+YPglLrly6lPf6whqoXSa20PmLsTh1OZbYn2xUXqJb94MnsSZ9CdRu3hWiXobq61TG0IG1Zhe8IMFtF5z8iaF0scpde+hdhwdo1317i+O+AuHdSH97RThI3gMRnEXK+MYNC1aBhqHZhsSfU7MQ8bmJVbgwbsSzM2VY4I6bPtndiUve2mPjnEgZUX5yOfGxzFgxVj5wIDBEnHahCVulT8zG0VW/GYVrN6Puf+P0GdhN6F0/MbPeJJfczcMtTjfMrZ0OUSUgKY0+58muxI+0czZc1CODPu9Zkjjf4vpVrbCcNz/9L7avWYmU5dnIrzmHfmlnWZdh2Z0rE1Gw9yC2V5VAl1+AvNJqHOqk3bbpT7TkT3eHzk3aSYsCI3r0ThfTPBrAvTs9LDEdK/LWoeqbE7j6lNzqxvtoLPD1O3F/YUjfLcu2JxJJe3rJPWzA9QOlyMrSofLkQxhN/WjKsd2aFsBZxBZC3BBps8hiyF8Yzkf6rnp8vaUc+cQfXelW1F2R+DPdnS7LVbpDkXXqOS3ofsKmnBXWz9Qsn6ulLImxemlE2izSHpEyIhhyGeGFtw8TNdfJvPLEgQARcTJQeByy97ThoXQ+TaI41teO3Vo3wV7oU67iM08wZezHmdKZwqnIOoIBNwFYDK159h20InkDGq7+BoO1nHH0ETqbypFoO2tUpaDqhy48GqIb2OYyBgz0nsf+9clQuLjblf88izHpIl6ORmD16E7E5yH9+DNzQJkXv99G+9HtWJ3oy/fhTuT2C4aC7VHSxcP9F9BnDs5Dnwc+60JjOeHjfFdADmfpkqFAGXluCLZZZNL0B4Z0ez/vcCf6hl5Z+SMFC7qAg6Up0/gjy9WIFOzs8xBQ6Bc9ou1fAsiMHZH2iJQRwY/LCMwDLEi8KPEfBwJHxHnw8+BnDjAHmAPMAeaATxxgEWfC+EQYXkH7bwXNWDKWzAHmwNtygEWcRZxFnDnAHGAOMAeClAMs4kFquLddvfHveQfAHGAOMAeCnwMs4izivAJnDjAHmAPMgSDlAIt4kBqOV9DBv4JmG7INmQPMgbflAIs4izivwJkDzAHmAHMgSDnAIh6khnvb1Rv/nncAzAHmAHMg+DkQXCIeHous2lbcNwcYmcLIwzbsynUTEEZGmEM0GdjU0o3BCSmQxhsMP2hHrW56hDShMpFLUXr0Gh5TRi9zwJOBbvywNRMRTpnH/FNPJBJK69DeO0hR7aR3ScFD2rAnz9eoboKE9RPOjglCCWX6blyXcPr9AJKdEnhYylDmttQS1LZ04t6gAcaJO6i2R+oT63uInC3maXF61EPglOFmZEU6Y0MR4JbqcY7i6Q+f1iJ8Ngs9v2LoDR8KMyvAZwvOXvoliI8sztJ71Ok48MdMrLs3xrvPAjgbfPk3vHNmDpg5EEQirsLn1VJoVgNuNenxxTo9GropxvhEFza7hmb1ZtzweBSfH4VpsgcNlTpkaMvw9aUhCnXagx0p1khpImXCopHT8ie9/7a5nrSsfFQd68Wk6QX+U2QNzeqvehQqxG04hlN121CypggFFXtw+ldKffr6JxTHKv08mP2Es5MNFKk7KAPXa4y4E/EwDZK3dWBwchA3T++HvqwI2VnLEKW29Uug70K2iMLCrFxkrdban1Xrv8Mvbygz2NkSe8z+T+NWouLwZUqNaxGhF7MScT9iKIePCMfIFrL9ChfARwRnye6ROThOceIfNxRjOYWIlcLELk1PR0KUD7kAeIL287gWXMAz7kGHe/CIuC3Rw5UKR5KU+I244jZJimfChtBvuijH86/fpuJTK2FDFhSjnQRm8Fi2OWyoSJk56pU4Qju1oWan+O3WuO5PGzP9Wo87l1fEmsu0oHmCQ+mzDNHqabD6CWdbm0NjinCi34DegwUo+nHEZSdOKTZzjuHJeDd2ZcwX3qXN6LuALWZgSOF7SzpoMTd4AjnRtgVDJFLr+zD0oBXfllfgFNl3ViLuNwzl8RHiqmIW/XKHjyjOmkK0v5pEV2ls0E2I7F5msQ82DgSNiDtSLiZh0c6bGH/Zg9q0ZEu60l7xdKWhi7bhrpQLe7NT3PXwxdhyl+rp+RJRFBtdpMwc+o2+h9z6/UeRs8Cyw1Ck7cUD4yja1saZBclf9UwnFblWlxRiz41x/PXkCDI1/h10/sLZ3GblQpRfHMJwRwVilW7ixlO87vXXKT694RH6BscwRUcbL5/9jOZt2VA5HUk4+u+h7wK2mI6hEipdM56bRnBurcuRRJjSspiQzfHtGXe/YSiAjxDHpP741C8P+AjiHBJfhZ+NBlyqSkN0dKTw4izYJk9ur3/nHsZzdngGlIiHqqOhXhCDSOmJjobCaSJXZDaRi3McrfnxSD/ab84IdWo1JeHoeEkZyeqR6pJn2SMhpNShT0mwHzVCt1CaYEgYkgotmc16qxErvVOkjCTaS6vRRrs109g9dJxpx72XL3GnXgeNrd3+qse6aw6N/xK3yItgTsoy0YN9K6NnNUG+F5zpCCBefwVjz8+iwJxS1Y2IawrQSh6QsauHsLFgFVIztShr6DZnsztf5pRbXloQyfRd1hbOngdlMqrv0HHE/VosivAwcN5CxP3GVRF8BDlmHw8i/fKCjwjOn6XU4NrACF5ZuTo1cBNHK1MR5pI8iCft2U3ajBvj5syBABJx6yRvz0A2jOZca1pLSTDtIk5/U8YgPikWnymifBdxutwTkVWLi88s2bcsz4T5v5OdxeZc2NIFIPkyEdDkN6F3fACX62uwq6kVvVIGtqfnsHGxzcXtr3qspA2PRkL6Kqxe/xUau4ZhmrqLfZkOjMQG9/vBOTSuDO2jI7hQnmBJAetGxEPjN+G25BXZsshaRlpALcdeWlAZb2yAxnnS99p3EVs4Br4ivR5/mF7hUpmXi4EiYufhSMJfXBXDR4RjTpOeQL884+MbztKFRWWSFptOP6K0tdPTsIpxlSdrxok5IMeBABLxCKiWapGbXwit+dEiyekijMNF6bRDC0+0uNPvirvTHYCooU5IwcKkRGiWfYOHJCa/bF9izdFsI46XMpo8nB6hs/V/p9nTb4YuKMBx2pm/vrgOkdN2Hf6qx4nQGh2aKV/6+DmdPcWqnLFtN8HfPc5KzK+45bRIcrmpPNUD/aIIhMRW4JaUx33/clqQWfsWFoeSbir/oAaJSg8D2LXvPtliHtK+p9zehlboorxMEAJi5wlvf3HVN3xE+CxyTOAFH59wdsJ2Xi5O0kU3Q1u+j1zlCVxsTDNOHzNOASTiMkS0XRbq3OC42Ba3EZ1uL7apEJVXg7rDu6CNV3m9XBOiXobq63TL3dCBtWa378x2uCszN2k77tGnXpfXxTj9JgbF1ybJTbsDn7sRIH/VY26jRovmYZoY2wv8OzH6CefQ+YuxOHU5ltifbFReIpwHT2JN+hKoJXzoXYcHaNd9ewvibEcQJJ6N9LdXF9ZYvSJueOHSd59sIbmfn9FCi+pXe7uJ+xYiLvWrgd4x8bZcnQU+snyW65cXfHzC2RlbstcpFnG+5Mc3398JB4JHxBVqJGyVPjEbR1f9ZhSu3Yy6/43T2fBN6F0/MbPeEpdc5MMtTrfHra7ysMR0rMhbh6pvTuDqU3KrG++jscDZtUrf08qVUa2wnKM//S+2r1mJlOXZyK85h35pZ1mXYd2d+6keZSIK9h7E9qoS6PILkFdajUOdtJs0/YmWfOvnbH4bIP7C2VV83ZyJ063ppD295Go14PqBUmRl6VB58iGMpn405ViPCUT6LmQLS3vmJu2kxZcRPXqni41O2IWoEpCURp9EZVfiR/J0GC7qkUGfSC1JnO9w+cti7S8MBfCRvv2W46r0xYVgv7ziI4TzfKTvqsfXW8qRT1zVlW5F3RWJq+xO/5h3i9z3d+ctCSIRJxDC45C9pw0PpbNnmojH+tqxW+sm2At9HlN85gmmjP04U+o6Wc9D+vFn5oApL36/jfaj27E60fUzLZEydE6fvAENV3+DwXq2bhx9hM6mciTag5n4qR5VCqp+6MKjIbrJbX6XAQO957F/fTIU7+KykF9wFhFxKqOky4n7L6DPHMCHdq/PutBYTv2yCaVg3+VtYWmP8p9nMSZdiszRuF0VK7KOYMB+V8JxDGBozfPN4+EvDOXwUQhyTLBfsvjIcZ5u1Ocd7kTf0CsrV6XARBdwsDTl3XBVdkH17iZPFibGNhA4EFwizgP2nbhjAoGI3AaeEJkDzAHmgO8cYBHnhQEvDJgDzAHmAHMgSDnAIh6khuMVq+8rVsaMMWMOMAc+NA6wiLOI8wqcOcAcYA4wB4KUAyziQWq4D201yf3hHRJzgDnAHPCdAyziLOK8AmcOMAeYA8yBIOUAi3iQGo5XrL6vWBkzxow5wBz40DjAIs4izitw5gBzgDnAHAhSDgSOiIfHIqu2FffNQT+mMPKwDbty3QRy8Qb0PC1Oj7rE6bYF7hhuRlak8yqUIl0t1eMcxTofPq1FuNt6KcNZaglqWzpxb9AA48QdVDtHhxNpszodB/6Y2abujfHWDGSRSCitQ3vvIEWjk8pJwTHasCfPS3IOTxiItEeEqLJtjsC8lV/hxI3HGDVnqprEi76L2FeUOD32vGw9Nnt4s4UgPsLv8sNOJKBwFsRHpM0iZez8UUKZvhvXKQud6fcDSLYHOPIDviIc5TIsuswBMwcCRMRV+LxaCqlqwK0mPb5Yp0dDN8XZnujCZteQqt4MFx6FhVm5yFqttT+r1n+HX94YMXS2xB5z/dO4lag4fJlSm1rE9YU7EQ/TIHlbBwYnB3Hz9H7oy4qQnbUMUWqldfAItjkyB8cpbvTjhmIsp/CdS81POhLsyV1UiNtwDKfqtqFkTREKKvbg9K+UJvP1TyiOtb1LZGIUbI8I8eXarEpFzZ0h/NZRh03rv0DBhlr832NafE3eQEWCU/x5uXqoLfK2EMRH4F3+caMFGs4i+Ii0WaSMg4eK1B24OvYaIyziLKYicwqXeWc8CQwRtyWMuFLhSG4SvxFX3CY3ERE0axkKv1rSMUpJN04gJ9omiJFIre/D0INWfFtegVO0E58p4pTeMecYnox3Y1fGfPc5u0XbrClE+6tJdJXGChsxYs1lWtA8waF013CwXvou2h6RwSTQ5hBl5LRY4uG5LXhhGsVZnVNqVNl6RGwxs89u8ZF9lw+88YZRIOLs0t4Z+Ii0WaSM9T2hMUU40W9A78ECFP04wjtxkTHFZYTnP/8stv003oPAbgEh4o7UjUlYtPMmxl/2oDYt2ZJmtHc2aUYlAyqh0jXjuWkE59a6uKbDlBZh9pTRieI/r79OccoNj9A3OIYp0xu8fPYzmrdlQ2XNtiXa5pD4KvxsNOBSVRqioyPdLwjsRCH3/ZJC7Lkxjr+eHEGmRpyIou0RGSC+tVlqYwSiK67RwuM+alLU9sEqVI+cLaYNIs/4CL3LDwMyEHF22NQ9PiJtFiljfo9yIcovDmG4owKxSndJbcQ5K8JFLsN4Mge8cyAgRFyR2USu7XG05scj/Wi/OePRqdWUGKPjJWUJq0eqahZEViaj+g65pe/XYlGEh997EnFNAVrJTTh29RA2FqxCaqYWZQ3d5gxq58ss+cxF2/xZSg2uDYzglfns2IipgZs4WpmKMJfEJaHxX+KWtYxpogf7VkbLCP70Pom2R2RAiLbZVpcipRqXKCnNH9/roHbql0/1yKTIlMPHp3fJiHmoOhrqBTGIlJ7oaChsaVJ9sPv7xFl6lzd8RLghUmaOQoV4/RWMPT+LAnPaXhZxETtzmVnM335YcH8suAeYiJMrVhmD+KRYfKaIeisRV6TX4w/TK1wq83JBzINwhMZvwm0S3DtbFjlcxqrl2EupR403NkBDQuWY9ETbHAFlkhabTj+i1Jtu0jKGRyMhfRVWr/8KjV3DME3dxb5MJ9e0DKl9b4/IwJJpM3k7IlbsprNRunNwXo+FHi83ydXjxSti67cwPgLv8oqlVZjsmcyG0ZzrsEPA4uwFH5E2i5QJjStD++gILpQnWMcFi/jHIhTcT5H58u8pExAi7nDlWXa5ZsKEJ1rc6Xdn406fh7TvKYexoRW6KC/AehDxkNgK3JLygu9fTosJ6+/D4lDSTe15UINEJe18krbjLpXprvKxzfNycZIuuhna8j2nttTo0Ey5rMfP6YTTX866PSIrXrdtjoA65wB66N5C/5kNiBfxlnjru8xOfNokIoKPCM5u+x4B1VItcvMLoTU/WiTZLyG+hd3fJ84u+IhwQ76MEvMrblnTi7r5AmSqB/pFTpcaRfrLZficmDnw1hwICBGfY7tU07nBcbEtbiM63V5sUyEqrwZ1h3dBG69yDwDVV//MiNcX1kDtjSSehIN+f3iAdt23tyDO5kqlso30t1dUp0qq06c2Oy0kNFqckhVxLZqHSejbC4RF3Lf2CGDojJubNs9NrMKFcSOenSnDAlrUCK3UvfXdJxEXwEcE59lMID7Z/e/DeRp/RNosUCZ0/mIsTl2OJfYnG5WX6CuSwZNYk74EalEezAZ3/o3YGGOcPjqcAkPEFWokbJU+MRtHV/1mFK7djLr/jdMnZjehd/3ELHK1eScrnS8Pt+QizA1p5ybtxD369x594rQb1DahCVElICmNPvXKrsSPtOM1XNQjgz79WpI431o+Ekl7esntbcD1A6XIytKh8uRDGE39aMqxuVZF2jwf6bvq8fWWcuTnF0BXuhV1V8hD4OxOVyaiYO9BbK8qgY7K5JVW41CnVOZPtOTH+HAuLtIeq9h6xVCgzXQWmnXqOXk6fsKmnBXWz+Ysn8+lLImxfisuUk84ZG0hhI/Yu4QWGrKTYIDhLISPSJtFyrgu1tid7h9OCS6CZbnJ9XyM9ggQEZfc53HI3tOGh3S+Kgn0WF87dmvdBHuhz8aKzzzBlLEfZ0rdi7Tyn2cxJl2Oy9G4XZUpso5gwH7m6XANGlrzHDtfJV2s238BfebgM0ZMPOtCY3kyFM4DSa7NdMs973An+oZeWd2QUiCXCzhYmgKF7QKYKgVVP3Th0RDdhje3yYCB3vPYv57e5XL5TZagcu2xH1V4wVCkzREp2NnnIajOL3pES94LkXqki2JythDBR/BdsviJTpKBhLMIPubjKYHxJVJmGkYs4n7jlCj3uNxHt9OW41jgiDiTk8nJHGAOMAeYA8wBnzjAIs6E8YkwcqtC/nd2aTIHmAPMgffHARZxFnEWceYAc4A5wBwIUg6wiAep4Xil+/5Wuow1Y80cYA4EKgdYxFnEeQXOHGAOMAeYA0HKARbxIDVcoK4KuV28Y2EOMAeYA++PA4Er4up/YON3xylHtVNENBZcXi0zB5gDzAHmAHPAzgGPIh6aUI07zt9S/6/Ue/Qzf4MaVYIuev/D3Sn41N91c308CTAHmAPMAebAB8CBT/a1/4I/KbypyTSBkd9v4uT2VYiUgnVQIpKFyykKV2Yx6ijxhyloRFwJxeJ18NivD8Bo7Kp6f64qxpqxZg4wBwKZA5+Y+i/gwJZS5BWXY9O+U/jPQS0inIVOuRhb7wePiH+Wsg1XX1F75frFYs6rcOYAc4A5wBwIcg58UmjOC+xlpSUn4qokFB78CY9Gpd38FEb6LuLfxUmO7F/qDBzsp/jkziFN3bjKQzRpFH70FgatXoHnDx7A4Ks7nTKfbbhG4UtfnIVsvwTCtwby6ovbxrsD5gBzgDnAHPjEnmrTk5B7E/GwGGhPPSPx/gOt32xEQXElvu7op///DCdtyTtERJxiX689PwKT8Vecqa1AQcmX2HnyZ7zyUcRD47/Erb8oheiBNMciwlO/BBKp8ADhAcIcYA4wB5gDgcwB+dvpXkQ8dNFX5stvv+1PcyQGUadjn3SG3lNtSeMpIOJzl9SYs449qP2H4xLbLC62KTKb8NT0CucLowRcRCpE59fgO28pTYPczRLIxOO28cTIHGAOMAfengNvJeIR+W14SSkzj69yzhamQcbJPylNZStyNWIiHpF/nnbdf+L7lfMc4jsbEc9oQL/ppaCIvz14TEDGkDnAHGAOMAf+Tg58Ivv5lpeduEXEn+OEi4ivmCbi6dj/xIhXHYWOC3MuAu22nlmIeOjnevxMO/pH36byZ2nsRRDwxvDk83dOPvxu5h9z4O058Il2gczFNrosVtVL7vHef2GB5B53EgebO/3x/nTP7vTwRdgs/f5BDRKV1t+7CHToomr0kPg+qc901DMLEZ+jTMbWXyj/9/NmyPZLoUJUXg3q2J3OYscLHuYAc4A5EKQc+MT4ezv+/a9S6ArXYv22Q/i+rhCaaWKtQVbzEF06e4Cj/ypBUfm/sLVymUVs6WJb7g/Wi217K1G07kt8e+Hp9IttCjUW1tzBX6ZxdDXoUZhfgLzKJjx2vrQm1XNK+t0wrjVWo7ioCPmuZYQAVkKZdRD36HKbbL/4YhsPWiFOvf1KmXcbjCFzgDnwrjjwydHrv2LEKH0eZsSboXs4X7cOMbYds3WSm5uwDk23/4RRiuBmHELvyTUOoadPzIrqOvGbwVLH+G+XcWDt4um3w6nMFw1X8ftLSxmT6TVePLmF79c6hVRVLUTe/vO4NzRpLUPBZ/64haPF8Qj1abKNgGrFvyDbL/7EjEXcJ17xJPSuJiGul7nFHJg9B+QvtvFEx2LHHGAOMAeYA8yBgOQAizgTMyCJySvz2a/MGTvGjjnw8XCARZxFnEWcOcAcYA4wB4KUAyziQWo4Xml/PCtttjXbmjnAHPDEARZxFnFegTMHmAPMAeZAkHKARTxIDccrc16ZMweYA8wB5gCLOIs4r8CZA8wB5gBzIEg5wCIepIbjFTivwJkDzAHmAHMgcEQ8PBZZta24P0phU6W85A/bsCs3zsdAL5FIKK1De+8gJqxBZZ7fa8OePJeAMZRp7cAftsAzjv92b4xHiFnUIzBv5Vc4ceMxRs2BcCbxgvKk7ytKxFy76L/PMn4kql9wtrVHibClepz704jh01qEuy6IZHEWqyckcilKj17DY2uwoImBbvywNRMRTpEF5csIcsNfi7r3ibPwuyIQkVqC2pZO3Bs0wDhxB9VJKusOTAyfEE0GNrV0Y3BCGhdvMPygHbU6X8eXH/nsL3txPbwTD1IOBIiIq/B59U0SXgNuNenxxTo9GroNME10YbN9khEZ+CrEbTiGU3XbULKmCAUVe3D61zcwvf4JxbFKB0kjc3B8iPKONxRjeXoGlpqfdCREWePIq1JRc2cIv3XUYdP6L1CwoRb/95gWF5M3UJHwN5TxG7n8hXM4Po1biYrDlyn1q2UR9MKdiMvhTP2SrScsGjktlBVv4jYaKnVIy8pH1bFeTJpe4D9FMZZFl0gZipUvxA2/YP0+cRZ8V5gGyds6MDg5iJun90NfVoTsrGWIUtvGhQA+4fEoPj9K46DHbIsMbRm+viSFZO7BjpRI8fHlF4xF5gMuwzv1D58DgSHiqkw0PDNi4koFoqy7q9D4jbgyacTTwyscSVFmMfgj1lymxcETHEp3mmQ0hWh/NYmu0liPq88QZeQ0L0B4bgtemEZxVjff/pv3WcYvg9FvOEcitb4PQw9a8W15BU7RTtytiMviLFCPeiWOUP1DzbkIs9nfGvf+aaM1YY5IGTfcccuNWXBshm3eJ85C71IiIucYnox3Y1fGfKu3SX5yc8UnhMZkF3mmfnXKEhiyoBjt5CEZPJbtGKeydpd/t1/47g9bch28Qw9wDgSEiIcmbcdd2s11VyVh0c6bGH/Zg9q0ZFTdkbKnbUWcS/Y0sQFOrsMlhdhzYxx/PTmCTCm3udUYIfFV+NlowKWqNERHRwpMahGIrrhGi4H7qElReyD1+ywzu0nQrziHKS24qbPR5EHEhXCWqyd8MfQ95AXpP4oca8Y9RdpePDCOom1tnKUNImWmDUTP3BDjlnf83yfOQu8K/xzrr7+GyfAIfYNjmCI3+MtnP6N5WzZUbseWe3xCF20zj9M7mxMdC1zCfstdGqc9XyIqzIKLkN0DfGL0Bw+4jtnNU4ybb7gFhIgrMpvILTuO1vx4pB/tJ/fsEE6tTsA/O17C9LQeqSrfOhUa/yVuWZO6mCZ6sG9l9DSh/iylBtcGRvDKWmZq4CaOVqYizDoJuZJIkVKNS2NG/PG9DuoAKOON5KHqaKgXxCBSeqKjoXCapP2Ns7kdXkTcJ5y91KNYWo02WiiYxu6h40w77r18iTv1umnZ9kTKSO2V44boBBIoOAvZVFOAVtotj109hI0Fq5CaqUVZQzctSsdxvswpCZEcPrTrr39KdnjUCN1CafFLYp9UiLrfpcV2NWKtXPPJ7izmvNNlDrwVBwJMxMlVrYxBfFIsZUGLmrWIzwmPRkL6Kqxe/xUau4ZhmrqLfZkON7hjoo6AMkmLTacfUarUIbQUWM9Y7aQiN+SK3bhKAj50Xo+FaneLifdZRm4xMx+r/jNizQInnVUPoznX0W/HhO8nnGVEXBxnb4uBCGjym9A7PoDL9TXY1dSKXrKH6ek5bFxsOyIRKWPFTpgb3rAOHJxFbBoavwm3pR30lkWOHbRqOfaS+BpvbIDGeWHqFR/ielYtLj6TLp/aLoROmP/3ZGcxVDMmY7nxJcdn/nfRRSWX+3i5EhAi7nAJOu0KwhMt7vS7s3WnW42q0aH5BaVIPaeD0tOKb14uTtJFN0NbvlOZCKhzDqCHzuX7z2xAvFtvwPssI0JSSsO6VIvc/EJozY8WSbbLetIuy35s4UecveygZ0wsbnG29stTPZo8nB6hc9h/p9nPXEMXFOA47cxfX1yHSEmARMq4s70IN9xyJnBwFrFpSGwFbpHQPt6/3JEiOCwOJd00vh7UINEl9bDdbh7xUUOdkIKFSYnQLPsGD6nuX7Yvcfpyw4Wr3uzOu7C32oWxeIvMix92mYAQ8Tm2yzmdGxwX2+I2otPtxTYVovJqUHd4F7Txts9jvBhJo0XzMAl0e4FnEacyp1xEfG5iFS6MG/HsTBkWeJjk3mcZvwzWd4GzLyLuBmd7vzzUM5cWHvco//zldTFOk10Miq9R3vn7O/A52UakjFv8RLgxG5F5nziLvIvKHB6gXfftLY77JYR3I/3t1YU1bnbQtgWw97ETol6G6uv0FYmhA2vjrF9tuF0szRxffuHzbGzDv+FFwwfGgcAQcYUaCVulT8zG0VW/GYVrN6Puf+P0WdFN6F0/MbPeTJZceMMtTjeWJcMoE1Gw9yC2V5VAl1+AvNJqHOocJHffn2jJt7nK5yN9Vz2+3lKOfCqjK92KuitSGWd3+nxknXpOk9NP2JSzwvoJmuVTtJQlMdYdx/ss46+VpJ9wli4vqRKQlEaYZFfiR/J0GC7qkUH4LEmcb3XZiuAsUI9qheXM9el/sX3NSqQsz0Z+zTn0SzvLugzL7lykjBA3ghFnEZtGImlPLx0ZGXD9QCmysnSoPPkQRlM/mnKsxy1C+FBcgMR0rMhbh6pvTuDqU3KrG++jscD5O3Exu7OI+4trXM/HzqUAEXHphnEcsve04aF03kkT9FhfO3Zr3QR7CU9A8ZknmDL240yp0y1Z82SegqofuvBoiG7ims/sDBjoPY/965OhsJ370U3dvMOd6Bt6ZS3zGs/vXcDB0hRHmYgU7OybGQzGXOcvekRLF3jeZxl/rhz9gTO1R5F1BAP2c1EHVobWPIvHQwRnkXqkMskb0HD1Nxis7zOOPkJnUzkSne4oyJYR4UYw4mzGWmDsKOmi6P4L6DMHU6LPOZ91obGcxoWtz0L4zEP68Wf0+9d48ftttB/djtWJTp9u+mD3j33i5f7z4sNfHAgcEffnBMp1scuMOcAcYA4wBz4CDrCIfwRG9teKj+vh3QNzgDnAHAgsDrCIs4jzap05wBxgDjAHgpQDLOJBajheDQfWapjtwfZgDjAH/g4OsIiziPMKnDnAHGAOMAeClAMs4kFquL9jxcfv5J0Gc4A5wBwILA6wiLOI8wqcOcAcYA4wB4KUAyziQWo4Xg0H1mqY7cH2YA4wB/4ODgSXiIfHIqu2FffNASumMPKwDbty3QSE8SrMkUgorUN77yBFiJOClEjBXtqwJ8856hRFEtNkYFNLNwYnpDJvMPygHbU6pzLztDg96iEgzHAzsiJdCa2EMn03rlM2KdPvB5DsnExFuF+UNSq1BLUtnbg3aIBx4g6qXSPa+WNRItwe0UHroe9CGIrZyzJ4KKLYUj3OUVz14dNahLtiIdwvP+As/C5vGEZg3sqvcOLGY4yaM+5N4kXfRewrSpwWp1yWq5RtTKQeC4YyfZftly/vEuUPl/s7xIHfGRy8CyIRV+Hzaik0qwG3mvT4Yp0eDd0Ut3miC5t9EjIV4jYcw6m6bShZU4SCij04/esbmF7/hOJYpcWlFB6P4vOjME32oKFShwxtGb6+NEQhJnuwI8UaoSo8CguzcpG1Wmt/Vq3/Dr+8oYxnZ0vsMeBtA0GRuoOyob3GyAwRF+xXmAbJ2zowODmIm6f3Q19WhOysZYhSW9vsD/E21yHYHh/e57HvQhgK2Iva8mncSlQcvkwpbS0LqxczRFywX37BWfBdchiqUlFzZwi/ddRh0/ovULChFv/3mBawkzdQkWCNVS7CVZF6pLbI9l2gX6Lvkus7/zu7t5kDQhwIHhG3JXq4UuFIkhK/EVfcJknxbQUVseYyLQ6e4FC6RaBDqN4u2vn8+m0qPrUSKWRBMdpJgAePZTtCVTqTjMLBlnSQ8A+eQE70dGENjSnCiX4Deg8WoOhHShXqvBMX6helgMw5hifj3diVMX9abnS/r5aF2iOOr9e+z9gpe8bQuZ+u9pqjiERqfR+GHrTi2/IKnKKd+AwRF+qXn3AWepcYhiHKSEf6UMIrPLcFL0yjOKuzxDwX5apcPZIXQ5Zjgv2Sf5dY3/3ObRYFIVFg3IOLn0Ej4o6Ui0lYtPMmxl/2oDYt2ZKutHe26UrJdbikEHtujOOvJ0eQqbEYL3TRNtyV8i9vdorNHr4YW+7Su3q+RJRz/mWrG1ela8Zz0wjOrZ3ulp+jXIjyi0MY7qhArNKah9pJxIX6RXHI11+nePCGR+gbHMMUufdfPvsZzduyoZLiuPtxchJqj+j7ZPo+vd1KeMTQ/j739jLXE6a0LG48ZEMT6pefcBZ6lyiG08pFILriGi0476MmRT1Lrlpc5q71SLHu5Tg2u365edes+u5fnvtzzHBdbJu/kwMBJeKh6mioF8QgUnqio6FwEihFZhO5SsfRmh+P9KP95qxjp1ZTUoeOl5Thqh6pbvN9eyZXaPyXuGU+Z6Rnogf7VkY7dri046h/Sn9/1Ajdwkj6O4lHUqElm1ZvNWJdhVOZjOo75JK/X4tFEc7vVCFefwVjz8+iwJyqcaaIC/VLU4BW8gKMXT2EjQWrkJqpRVlDtznr2/kyp9zggpPj+8FZvu/TiO8RQ+vCypu9nPvtQcTfJ85C7xK0lTNGipRqXKIEQX98r4PatpD0lav0Xrf1CHBsNv1y+65Z9P3vnCT53SzSgcyBABJxq8DZM2MNoznXmiZRmnjsIk5/U8YgPikWnymiZi3ic8KjkZC+CqvXf4XGrmGYpu5iX6btfeRazKrFxWeWjE+WZ8L838nO4hn5lxXp9fjD9AqXyqbvwkPjytA+OoIL5Qn29Jyr/jPdnS7Sr9D4TbgteQa2LHK4VlXLsZcWFcYbG6CZ4RnwNujeD84ifZ8mUB4wtJfxai+n/sqKuGf++AtnEZv6NikQH1fspjsVdN/ivB4LnS9FSm5wYa56rkek7771y1ubWRR8sz/jxXh55kAAiXgEVEu1yM0vhNb8aJEUZb28I7m4k7abXdzdVU47z/BEizv97mzd6VZgNDo0U07s8XM6SxpN+6OGOiEFC5MSoVn2DR7S+3/ZvmTazeA5inlI+57ykRtaoYty/q0S8ytuOS0CXG6yT/VAvyhCqF8hsRW4JeXP3r+cFi7Wd4TFoaSb6nxQg0SlL4P8feAs1ncHzp4w9NAvj/YScad75o+/cPYvVyOgzjmAHrr70X9mA+I9epzkuOq9HpG+i/dLtM2+8JbLspAxB9xxIIBEXMZAtks1nRscF9viNqLT7cU2FaLyalB3eBe08Sr5M2ONFs3DRhjaC1xE3NKmEPUyVF+nm/CGDqw1u8Wd2iq5M58Z8frCGqhd3ISh8xdjcepyLLE/2ai8RPUMnsSa9CVQS+Ir0i8qc3iAdt23tyDO5sqnHWcj/e0VvVflT/ekSHvs7/OMs1DfbfV4wdDtxOXNXh524u8VZz9hKPV9bmIVLowb8exMGRYILNY8cVW2HhGOCfZL9l3+5CvXJT+/MUYfNEbBI+IKNRK2Sp+YjaOrfjMK125G3f/G6Tz7JvSun5hFrsbJIcvOd7glF2HOJFYmomDvQWyvKoEuvwB5pdU41Ek7adOfaMmPsZ6L0/fGielYkbcOVd+cwNWn5FY33kdjgculNWmSTdqJe/SeHr3TJTiPg2bmmfgcoX5FImlPL/6iz+uuHyhFVpYOlScfwmjqR1OO48jBPyt1P+E8AwN3fbcshrxiKGQvWmipEpCUloGl2ZX4kbwqhot6ZKRnYEnifOsRhEi//IWzyLusC0FvXKU7FFmnntPi8SdsylmBpdQf25OyJMbqERLhqkg9In0X6ZfIu3hH5Z+xyjgyjuEIIhEnwobHIXtPGx7S2aAk0GN97ditdRPshT73Kj7zBFPGfpwpdRFXVQqqfujCoyG67W0+6zZgoPc89q9PhsJ+tjwP6cefmQPBvPj9NtqPbsfqROv34S7ipPznWYxJl+xyNAKrPQ9CJtIvJV3i238BfeZAN0ZMPOtCYzm1+V2sskXaI73XG84+iLhXDIXsRXcmso5gwH5/wXF0YWjNc3hXRPrlL5xF3iWHYUQKdvZ5CCj0ix7RZq+MAFeF6qG6RPou1y/Rd70L3nKdAnMQC/+HJvzBJeI8SHmQMgeYA8wB5gBzwM4BFnEmA08IzAHmAHOAORCkHGARD1LDfWguIe4PuzmZA8wB5oDvHGARZxHnFThzgDnAHGAOBCkHWMSD1HC8YvV9xcqYMWbMAebAh8YBFnEWcV6BMweYA8wB5kCQcoBFPEgN96GtJrk/vENiDjAHmAO+c4BFnEWcV+DMAeYAc4A5EKQcCC4RD49FVm0r7psDnkxh5GEbduW6CfbizRjztDg96iGAxnAzsiKllVAkEkrr0N47SBHipLKv8fxeG/bkzYzYJuVhDluqxznKYT18Wotwl3eHRC5F6dFreExZyMxBWga68cPWTEQ4ZUIL0WRgU0s3BiekMm8w/KAdtbrp7xKpR2gV6w8M7X2k7G6pJaht6cS9QQOME3dQ7Ro9T8oA56mMkC1sK1PvOM8R6Zc6HQf+mGn77o3xvuVoF3mXTxOCEsr03bguccQ51/y0OuTKeMNHkM8+9UuuPb7vKIT46xOu3AbG9MPnQBCJuAqfV0thVw241aTHF+v0aOimOOQTXdg8Qzi8GC48CguzcpG1Wmt/Vq3/Dr+8oQxRZ0uscdlViNtwDKfqtqFkTREKKvbg9K+UavT1TyiOVdpXrJ/GrUTF4cuUItUiDC9cRTwsGjktf1Ibb6OhUoe0rHxUHevFpOkF/lNkDfEaHo/i86MwTfaYy2Roy/D1pSEK89qDHSnWKHEi9QhNbn7CUHpXmAbJ2zowODmIm6f3Q19WhOysZYhSO/CRLSNki3DI4qwQ7FdkDo5TON7HDcVYbg9hmo4Ep0Q78pOe4LuE7GHhqSJ1B2Uoe40RLyLurYwIPvJ89q1fIm2Wx/LDn2AZA7bxu+ZA8Ii4LfnClQpHApT4jbjiNgGKD8Sh0KElHSSigyeQE+0kQC6TcMSay7SAeIJD6bbwq5FIre/D0INWfFtegVO0E58h4uqVOEJ/H2p2it9ujZX9tDHTHDI1hPrQRXnNf/02FZ9a3xmyoBjtNKEPHsu2hFUVqEeIKH7DkNJM5hzDk/Fu7MqY72EXK1LGxU5ubSGAs2i/NIVofzWJrtLY2bsORd8lKOKhMUU40W9A78ECFP04PU2tzabeywjg46YtM/jsQ79E2izER0GMuC4f5jPGdPZjO0ixCxoRd6RBTMKinTcx/rIHtWnJllSkvbNNRaqESteM56YRnFvrzlUuDR5yBy8pxJ4b4/jryRFkapwGVJjSImCeMmeFL4a+h1z//UeRs8CS/UyRthcPjKNoWxtn/m3oom3mFKt3NjvFeKffbblL/er5ElFSPHeBekQmOr9hGP451l+n2POGR+gbHMMUHQG8fPYzmrdlQ2U7JhAp4+Iu9mgLGZxF+xUSX4WfjQZcqkpDdHSkby50a1tF3yVijznKhSi/OIThjgrEKj3E1RcpI8fDaTi757Nwv0TaE6SToZDNuG8fnUgGOi8CSsRD1dFQL4hBpPRER0PhdG6syGwit/U4WvPjkX60n9zXlHRkNSUF6XgJ09N6pHrMs+xlFatMRvUdcpPfr8WiiJnlQuO/xC3aJZsTpUz0YN/KaPcTvycRl0R7aTXaaDduGruHjjPtuPfyJe7U66Cx9U1Kw/mU/v1RI3QLJWGhSTapEHW/S4uTasRay8nWIzC5+A1DTQFayVMwdvUQNhasQmqmFmUN3eYMc+fLrPm6Rco4t1nGFuaB5AFn0X59llKDawMjeGW16dTATRytTEWYPfGN/I5H9F3yA1+FeP0VjD0/iwJzelt3Ii5SxqnNXngotccbn8X65WN7BDgpj5O8TbgOxuhj5kAAibh1ErNnohpGc64jzaZjkqG/KWMQnxSLzxRRbyXiivR6/GF6hUtlHnbh4dFISF+F1eu/QmPXMExTd7Ev003qT4+TZwQ0+U3oHR/A5foa7GpqRa+Uge3pOWxcbHPLk9s5qxYXn1myk1meCfN/JzuLrbnCReqRH8j+wjA0fhNuS96DLYusaT7p3arl2EsLD+ONDdCQKIqUcR54srYQEnFRbkRAmaTFptOPKL3rEFoKbClo3yOGcWVoHx3BhfIEK4YzRTxUoMy0yUtGxOd44bMIN3xuD4s471qZA++cAwEk4hFQLdUiN78QWvOjRZLThSOHu8+605PIEZ5ocaffnY07fR7Svqc84oZW6KLkJ+85Gh2aKU/1+DmdI7WljaCeJk9NHk6P0Hn3v9PsKUNDFxTgOO3MX19ch8hpO0A11AkpWJiUCM2yb/CQRPKX7UssOaN9qsdzX/yFYUhsBW5R+x7vX04LKev7wuJQ0k22eFCDRCWd9QuUcQiQoC084Dzrfs3LNeedN7Tlz7Sph8ln1u9yOTqYX3HLadHmcmN+qgf6RSrIl7Ec0dgfORF3LuvCZ/l+KX1vD0/g73wC/5h3oNx3y9gPIBGXEVLbxZvODY6LbXEb0en2YpsKUXk1qDu8C9p4lfuBJLmxn5GYXlgDtchko9GieZgm/PYCYRGfm7Qd9+jztMvrYpzaEIPia5Pkwt+Bz0nsXIkYol6G6ut0697QgbVmN2s4ZlOPW4L7C0Oq5/AA7bpvb0Gc7ViABKSR/vaK8FRJeIqUseEuagtPIuVTv5wwJ5ue8lHEpX41EG8m3pKHofMXY3HqciyxP9movER2HzyJNelLoCZuiJSZvYi78FmgXz63R2RccRkWeubAW3EgeERcoUbCVukTs3F01W9G4drNqPvfOJ1V34Te9RMz6w1wySU93OJ0M9yJLHOTdpLAGtGjd7pQZvt3ZSIK9h7E9qoS6PILkFdajUOdtGs3/YmWfIfrNUSVgKS0DCzNrsSPtEs3XNQjgz5dWpI43+IiVa2wnG0//S+2r1mJlOXZyK85h35pF1uXYd2d0/e9ielYkbcOVd+cwNWn5FY33kdjgZOLX6geAW+C3zCMRNKeXnJFG3D9QCmysnSoPPkQRlM/mnJsxw0iZSxt9moL+ndZnIX6NR/pu+rx9ZZy5JNNdaVbUXdFsqlv7vQ5Qu+y2kKAhw4R9nCxbdoE576MLD5CfPZhfNnbJNJmEV5yGd5VMgdmy4EgEnHJfR6H7D1teCidK5MQjvW1Y7fWTbAX+lSp+MwTTBn7cabUjUjTJKT851mMSZfjcjQzV0GqFFT90IVHQ3QD23xGbcBA73nsX58MhZMLXJF1BAP2c2yHS9TQmmffrSuSN6Dh6m8wWMsZRx+hs6kciWobaech/fgzc0CZF7/fRvvR7VidaDsvdxBbvh7BQeAvDJV0qXD/BfSZA+/QzvRZFxrLCZ9pl9UEysjZgv5dBGdZbtBt+bzDnegbemW1qRTA5wIOlqZMs6nQQPIXhgICPb097kVTFh9BPstiOGPHxCIuxBfeab7VTpMx9j63B5eI82DgwcAcYA4wB5gDzAE7B1jEmQw8ITAHmAPMAeZAkHKARTxIDccuJsHjA7YvT87MAebAB8wBFvEP2Lgs9Cz0zAHmAHPgw+YAiziLOK/SmQPMAeYAcyBIOcAiHqSG49X1h726ZvuyfZkDzAERDrCIs4jzCpw5wBxgDjAHgpQDLOJBajiRFRqX4ZU8c4A5wBz4sDkQXCIeHous2lbcNwcYmcLIwzbsynUT7MWbMM/T4vSoS6xqW8CW4WZkRUoGj0RCaR3aewcpQpxUVgoM0oY9eZ7SlSqhTN+N65TZy/T7ASTbArkIvctGMMpellqC2pZO3Bs0wDhxB9WukeikDGeyZWQI6w8MCd+QyKUoPXoNj6U+S8FeBrrxw9ZMRDhlnpujTseBP2Zi3b0x3k02OA8YSrYUqEeoPWZeUIS8pXqco/j1w6e1CH9Xizg/4TxHth5fuCrCHy/4CPFZpD0iZT7siZeFle3rLw4EkYir8Hm1FHbVgFtNenyxTo+Gboo1PdGFzTPEzgtBwqOwMCsXWau19mfV+u/wyxsjhs6WWOOyqxC34RhO1W1DyZoiFFTswelfKWXp659QHKuc4XZSpO7A1bHXGHEVcaF3UVvDNEje1oHByUHcPL0f+rIiZGctQ5Ta6V0iZWQFyU8YhkUjp+VPwv42Gip1SMvKR9WxXkyaXuA/RU4ZwSJzcJxikz9uKMZyCke71PykI8EpsY2NyB4xlPokV49gez6NW4mKw5cppa1lYfHinYm4n3BWiNQjyFUB/sjiI8RnkfaIlOFJ3l+TPNfzYXMpeETclqDhSoUjAUr8RlxxmwDFB6NRiNaSjlFKPHECOdEzBdo2ACLWXKYFxBMcSp8eEjU0pggn+g3oPViAoh9Hpu/EXUXV7bsoFWnOMTwZ78aujPnu85XT7lG+jECf/YWheiWO0E52qNkpLr01TvjTxkxH6FVNIdpfTaKrNNbreZsshnL1CLUnEqn1fRh60Ipvyytwitr/zkTcXzjPsp6ZXBXhzyzwecux4ywunsYXC5DAuJZdvHMdHzKPgkbEHakSk7Bo502Mv+xBbVqyJRVp72xSkVpcqypdM56bRnBurSdXObkglxRiz41x/PXkCDI1TgNCuRDlF4cw3FGBWKVcHGkP76KY3uuvU4x2wyP0DY5hyvQGL5/9jOZt2VDZXNMiZQQGst8wDF8MfQ8dafQfRc4CS6Y1RdpePDCOom1tnH0hEhJfhZ+NBlyqSkN0dKT7BYoAhrL1CLZnTpjS0gZfUnYK4Oo6QfgLZ9/r8cBVUf74hM9bjh07rl7G1yyw/5Ana+4bL0bccSCgRDxUHQ31ghhESk90NBRO56uKzCZyg46jNT8e6Uf7zdmnTq2mBBsdLylLWD1SVbMwsDIZ1XfITX6/FosiZv4+NP5L3DJaz3QnerBvZbSTEKkQr7+CsednUWBOGSoj4p7epSlAK7nhx64ewsaCVUjN1KKsoducre18mTV3ukgZgQnPnxgqllajjXazprF76DjTjnsvX+JOvQ4aJ5t9llKDawMjeGXFcGrgJo5WpiLMnkRGDEP5emgRIdAe+wDwg4i/D676Yi+vXPWVPyL4vNXYsYw17+NrFuNZYAywEDKuHxoHAkjErSJozwo2jOZcW1pLmqTtIk5/U8YgPikWnymi3krEFen1+MP0CpfKPOzCw6ORkL4Kq9d/hcauYZim7mJfpqVNoXFlaB8dwYXyBEvaURkR9/Su0PhNuE19vrNlkbUeqku1HHsphanxxgZoSPBEyogQ038YRkCT34Te8QFcrq/BrqZW9EqZ5Z6ew8bFMzOwzaELecokLTadfkTpSx2pP33F0FM90t99ao+ISHkVhPfDVZ/s5Y2rAhybxh8BfN5m7Njf5aXNInzmMizIzIFwBJCIR0C1VIvc/EJozY8WSU4XoByuRevuVJpkwxMt7vS7s3Gnz0Pa95RP2tAKXZTAYNDo0Ew5w8fP6SjNqBLzK25ZU1q6uek+1QP9Ioub2fJ4fldIbAVuSfnF9y+nRYm1fFgcSrqp3gc1SFTSTXCBMiJk9huGmjycHjHi13+n2c+/QxcU4DjtzF9fXIdIp3St09o1Lxcn6aKboS1/Fhg62WhaPfR3X9sjIFLe8Xw/XJ21vaZxdRb8kcXnbcaOh7Hm0mYRPnMZgXmLvRMf/PfvASTiMoS0XfLp3OC42Ba3EZ1uL7apEJVXg7rDu6CNV7k3ItVX/4xE58IaqEWIrtGieZgEqL3AnCs8dP5iLE5djiX2JxuVl+i2/OBJrElfAjWJr32S8fYu+rfDA7Trvr0FcTZXNE2ijfS3V9Q2ldQ2kTIiffAThnOTtuMefXZ3eV2ME7YxKL42SUcTO/C5c9+d20UYnrKLuI8YeqnH5/bIitRbTo5+wlmyewNxdEKI805tduGqz/yRw+ctx45b8XVtswifucwHL1C8UJOfi4JHxBVqJGyVPjEbR1f9ZhSu3Yy6/43TZ043oXf9xMx6U1r6jGi4xekGtdOgn5u0k4TIiB59osONbft3ZSIK9h7E9qoS6PILkFdajUOdtGs3/YmWfKdPqKZNIp7PxL2+i75JT9rTS25mA64fKEVWlg6VJx/CaOpHU47tOEGkjLyx5/gLQ9UK1JG73/T0v9i+ZiVSlmcjv+Yc+iWPQl2GdXc+H+m76vH1lnLkE4a60q2ouyJh6HCnzxyg7jAUqEeoPbQjVSUgKY0+c8uuxI/kVTFc1CODPntbkjh/JgfeSiD8xVWBeoS4KsYfUXzeeuwItVmEz1yGRY45EEQiLrnP45C9pw0PpfNXEoyxvnbs1roJ9kKfvhSfeYIpYz/OlLoRaZqglf88izHpclyOZuZqVpWCqh+68GiIbo2bz+gNGOg9j/3rk6Hw5Cr2cibu9V2SWCjpgt7+C+gzB7GhndezLjSW07uchUSkjIjw+AlDRfIGNFz9DQbrHQbj6CN0NpUj0Rbohm5E5x3uRN/QKyuGUsCcCzhYmuIbhoL1yLZHukGfdQQD9jsXjmMQQ2ue2bvi1wnRTzjLcl6UqwL8EcXnrceOaJv9bROuz78cZzwDAs/gEnEmTUCQxq9ixzZlmzIHmAPMgVlzgEWcyTNr8rCY+3n3zlxkLjIHmAM+coBF3EfAWLhYuJgDzAHmAHMgUDjAIs4izitf5gBzgDnAHAhSDrCIB6nhAmUVyO3gHQlzgDnAHPj7OMAiziLOK3DmAHOAOcAcCFIOsIgHqeF45fv3rXwZe8aeOcAcCBQOsIiziPMKnDnAHGAOMAeClAPBJeLhsciqbcV9c1CUKYw8bMOuXDfBXoSNoYQyfTeuUxYx0+8HkGwLVGL/PaVJTC1BbUsn7g0aYJy4g2rn6HBy7ZmnxelRN7HVpYAjw83IivTzalauPUK4RGDeyq9w4sZjjJqzj03iRd9F7CtKxFzb70X6JVJmWnvezhYhkUtRevQaHku2lALmDHTjh62ZiHDKqua3lbNfcKYIcpoMbGrpxuCE1OY3GH7Qjlqdu2Q8SoQt1eMcxaYfPq1FuFs7euGqT7bwxnkBbghxzM+853eyAH/EHAgiEVfh82op7KoBt5r0+GKdHg3dFKt8ogubXcOuChpUkboDV8deY8SdiIdpkLytA4OTg7h5ej/0ZUXIzlqGKLXSOmAE2hMehYVZucharbU/q9Z/h1/eGDF0tsQeA94/4iLQHhFcVKmouTOE3zrqsGn9FyjYUIv/e0yLpskbqEiwJnUR6ZdIGaf2vJUtwqKR0/InceE2Gip1SMvKR9WxXkyaXuA/RZ7C5M5WSPyEc3g8is+PEq495jZnaMvw9aUhmIw92JHiyAT3adxKVBy+TGl4LYuTF+5EXI6roraQq0eEGyIc4zIsuswBv3EgeETclgziSoUjAUr8RlxxmwBFfoIOjSnCiX4Deg8WoOjHEZeduBIROcfwZLwbuzLmO+UQn57UxJycwpf2UDjYkg6auAdPICfathiQb6uQyPsRnxBl5LRY4uG5LXhhGsVZnSM17LQ2ifTLS5m3toV6JY7QLnWo2SlOvjV+/tPGzOnha9928vATziHE3S7ydPz6bSo+tbYpZEEx2mlBOXgs29rmSKTW92HoQSu+La/AKerjTBEX4Kprn93aQqwen7nxtnjz7/022QvNI4x30OEdNCLuSMuYhEU7b2L8ZQ9q05ItqUh7fUxFqlyI8otDGO6oQKzSTdINite9/jrFTTc8Qt/gGKbI1fny2c9o3pYNldU963t7lFDpmvHcNIJzaz3kL3+LAeR7e0QXDxGIrrhGHpD7qElRuyG4SL+8lPGDLeaEL4a+h7wF/UeRs8DiLVCk7cUD4yja1sa5X4TNEmt/4Ry6aBvuSnnkNzvF9qd+bLlLfO75ElG2GP1hSkv7PWUWE+Dq9Mnbgy18rkfijxw3RDnG5VhgmQOz5UBAiXioOhrqBTGIlJ7oaCiczjMVmU3kUhxHa3480o/2m7NhnVpNiUM6XlI2rXqkqkRJoEK8/grGnp9FQZw04bsRcU0BWmlHNHb1EDYWrEJqphZlDd3mDGrnyyz5zH1ujzIZ1XfeUKrOWiyKEG3r9HLvB5/p71SkVOMSJZz543sd1O6Sv4j0y2MZ/9jCLNpLq9FGO1XT2D10nGnHvZcvcadeB80szsTfC85SOs+n1N5HjdAtjCShprPopEJLdrjeasS6ttuTiAtwddrk4MkWvtYjYS7HjVkulGY7mfHvZjevMG7BjVsAibhVTO1ZpobRnOtw3zpEk/6mjEF8Uiw+U0T5LOKhcWVoHx3BhfIEq8t4poiHxm/CbWmXtGWRw62sWo69NMEab2yAhsTM1/Yo0uvxh+kVLpXNdhf+fvBxDGhyr67YTXcGyE19Xo+FMy79WYgv0i9PZfxlC2lHqMlvQu/4AC7X12BXUyt6pUx3T89h42LH+bLYZPW+cCZ8s2px8Zklc53lmTD/d7Kz2JJH3vnxIOIiXHWux6MtBDjvKzfE8A7uCZT7yPb7uzkQQCIeAdVSLXLzC6E1P1okRVkvUtFk5nBjWnbCZuDCEy3u9Lui7nQl5lfccpo0XW6OT/VAvygCIbEVuCXlxt6/nBYK1neFxaGkm8o/qEGi0tf2zEPa95RL29AKXdRsSf8+8LG1LQLqnAPoofsG/Wc2IN6jl0OkX57K+M8WczR5OD1C58v/TrOff4cuKMBx2pm/vrgOkR7Tx7qzxfvEWXq/GuqEFCxMSoRm2Td4SLz7ZfsSx5cANv55EHERrjomGc/2Eq9HlBuz5Tn/7u8WBX5/cHEwgERcBjjbhaLODY6LbXEb0en2YpsKUXk1qDu8C9p41bQdTej8xVicuhxL7E82Ki/RLffBk1iTvgRqEug59K7DA7Trvr0FcTa3Jk2ijfS3VxfWWHZJvrRHcp3SJbjX9Fv1u3Ix+tIehWd8pAE8N7EKF8aNeHamDAskPDy1WaRfXsr4yxZzk7bjnuk1Lq+LcWprDIqvTdLxxQ587q0PvtrDjzg74xqiXobq68RDQwfWmo95xHbiQly11eXNXiKc94UbvuLK5YPuQhWLfWCIffCIOO1YErZKn5iNo6t+MwrXbkbd/8bps6Kb0Lt+Yma9mSy5JodbnG4su50o3JyJKyKRtKcXf9HnbNcPlCIrS4fKkw9hNPWjKcfm4hdvz9yknSQyRvTonS4x+X3SEm/PHK/4zEfWqeckJj9hU84KLE3PsD8pS2Km7RBF+iVSxjEZzNIWqhWWs+Sn/8X2NSuRsjwb+TXn0C95U+oy/Hs73W88pG+/E9OxIm8dqr45gatPya1uvI/GgunHLSGqBCSlkQ2yK/HjCyMMF/XIIJssSZxv9UiJcNUy2Xi3hUg94tzgCT4wJni2w4dvhyASccl9HofsPW14KJ130gQ91teO3Vo3wV7oE5riM08wZezHmVI54XQnHPQuJV2a238BfebAMvQp2bMuNJYnTxcEwfYo/3kWY9JFvBzNu11tC7Znjjd8IlKws89DgJpf9Ih2unAl0i+RMt5FXMwWiuQNaLj6GwzW82Xj6CN0NpUj0cNZ/ltNbv7AWTEP6cefEbde48Xvt9F+dDtWJ848v1dkHcGA/czcYRdDax6UtoWgCFeprKwt5OrxgRtvha/fF7gf/kTOeH+8Ng4uEefB/W4XAYwv48scYA4wB4KKAyziTNigIizvOD7eHQfbnm3PHJjJARZxFnEWceYAc4A5wBwIUg6wiAep4XhFyrsS5gBzgDnAHGARZxHnFThzgDnAHGAOBCkHWMSD1HC8AucVOHOAOcAcYA6wiLOI8wqcOcAcYA4wB4KUAyziQWo4XoHzCpw5wBxgDjAHAkfEw2ORVduK++bgKlMYediGXbluArkIiS5Fw1qqxzmKnT18Wotwl9+ERC5F6dFreEyZysyBXAa68cPWTETYA5lEIqG0Du29gxQhTirzGs/vtWFPnlM0rXlanB71EBRluBlZka7kUkKZvhvXpXf+fgDJTkFIQjQZ2NTSjcEJqb43GH7QjlrdLBKl+AlD8fZQ5q3UEtS2dOLeoAHGiTuodoqeJ49zOETKzBHol3ibZQa9wLuEJk51Og78MZMf3RvjXVKjeuNqBOat/AonbjzGKOUeN5km8aLvIvYVJU6PrS7SZpH2iJQRGn88sQpxhLHk3b8fOBAgIq7C59VSSFUDbjXp8cU6PRq6KY70RBc2u4ZUlen0p3ErUXH4MqUttUygL1xFPCwaOS1/Ut230VCpQ1pWPqqO9WLS9AL/KYqxTrAqxG04hlN121CypggFFXtw+ldKI/r6JxTHKq3JV6KwMCsXWau19mfV+u/wyxvK+nW2xB7f3TaYFak7KCPYa4y4inh4PIrPj8I02WNuT4a2DF9fGqIQnD3YkeJLBi4/YSjanjANkrd1YHByEDdP74e+rAjZWcsQpbbiI4KzSBmK8y7LDdE2yw4YgXfJ1mEVsMgcHB+isK8NxVhuD12bjgSnpD6yXFWloubOEH7rqMOm9V+gYEMt/u8xLXInb6AiwRZfXbDNAu2ZI1JGtP9cjgWKOfBeOBAYIm5LKnGlwpHcJH4jrrhNbuJtlR+J1Po+DD1oxbflFThFO/EZIq5eiSP096Fmp5jq1ljiTxszPcbZjlhzmRYZT3Ao3YOwUijTkg4S48ETyIm2CpmVxKExRTjRb0DvwQIU/TgybSceQv3sol3Wr9+m4lNr+ZAFxWgnsR88li0e99tPGIq1h9Jo5hzDk/Fu7MqY77KztNpHBGeRMgL9EmuzwO5Q4F3COyxNIdpfTaKrNNbDQBbgKvEhRBnpyNpH/z88twUvTKM4q7PG8Bdts2x7CB+RMjwxv5eJWZhnbI+P3h4BIeKONKNJWLTzJsZf9qA2LdmSZrRXNM2odZIOU1pExUPqxjnhi6Hvod1M/1HkLLDsZhRpe/HAOIq2tXFuBIlcxksKsefGOP56cgSZGndioIRK14znphGcW+viBlcuRPnFIQx3VCBW6SZ3+aJtuCvlLt/sFOOd2rjlLvW950tECabR9BeGoSLtCf8c66+/piQpj9A3OIYpOgJ4+exnNG/Lhsp2JCGCs0AZkX4JtVlgshN5l+jkGhJfhZ+NBlyqSkN0dKT7hY4cV2e0OQLRFddoMXkfNSlqM3dF2yzSHpEyov3ncgKLRgFOMo6MoxwHAkLEFZlN5P4eR2t+PNKP9pMbnJKFrKYEJB0vKTNVPVI95rP2YmBPIi6J9tJqtNFu3DR2Dx1n2nHv5UvcqddB45TcwzxBxn+JW+azSHomerBvZbT7yViZjOo75G6/X4tFEc5tUiFefwVjz8+iwJxe0k2yFSk95FOq/1EjdAulyZ4WDUmFlqxcvdWIdWmTJ4P6DUOR9mgK0EqegrGrh7CxYBVSM7Uoa+g2Z5g7X+bI9y6Cs1wZoX6JtFlgwhR6l0A9ko0+S6nBtYERvLLyZ2rgJo5WpiLM3aLMC1ed7a1IqcYlSv7zx/c6qK31iLZZpD0iZeQmFP53Fh3mwPvlQICJOLkIlTGIT4rFZ4qodyTiEdDkN6F3fACX62uwq6kVvVJWtKfnsHGxi6s8PBoJ6auwev1XaOwahmnqLvZl2lKROgylSK/HH6ZXuFQ2fRceGleG9tERXChPsLpE3WVMI9d0Vi0uPrNkS7M8E+b/TnYWW3KXCzyOyfxtMZRvT2j8JtyWvAdbFjlcvarl2EsLD+ONDdCYBUYEZ/kyYv2Sb/P7xdDZZhFQJmmx6fQjSm07hJYC270LpzKyIk79W7Gb7lTQMdB5PRY6XYoUw8fH9pDtZNsswEkRzLmM2PhmnBgnTxwICBF3uAQdu7g54YkWd/pdH93ptsnF08SoycPpETqD/nea/bw5dEEBjtPO/PXFdYj05L7W6NBM+ZzHz+kcKSDN75qHtO8HybXcCl2UM9GUmF9xy0mYXW4qT/VAv8h2OUn6nRrqhBQsTEqEZtk3eEgi+cv2JdNvIXuZOP2Poef2hMRW4JaUq3v/clps2Y4x4lDSTX18UINEpXS+KoCzQBnf+hVoGDrxYV4uTtJFN0Nbvgt/vBz9mLGNgDrnAHrofkj/mQ2Id/FK+YaPYHtsNvXWZhZxocU1iy+L77vmQECI+Bzb5ZzODY6LbXEb0en2YpsKUXk1qDu8C9p4leeB5EHE5yZtxz36ZOzyuhin38ag+NokucN34HNJgNxNUBotmodpEm4vmD4JS67cZ7QAuLAGapffhc5fjMWpy7HE/mSj8hLduh88iTXpS6B2864Q9TJUX6cyhg6sNbvgBQfBu8BQuljlrj30rsMDtOu+vQVxNnc/4d1If3tFOEjeAxGcRcr4xg0LVoGGodmGxJ9TsxDxuYlVuDBuxLMzZVjgjps+2d2JS97aY+OcSBlRfnI58bHMWDFWPnAgMEScdqEJW6VPzMbRVb8ZhWs3o+5/43QOfRN610/MrDfJJXfzcIvTDXNrp0NUCUhKy8DS7Er8SDtnw0U9MugTnyWJ8y2uX9UKy3nz0/9i+5qVSFmejfyac+iXdpZ1GZbduTIRBXsPYntVCXT5BcgrrcahTtptm/5ES/50d+jcpJ20KDCiR+90Mc2jAdy708MS07Eibx2qvjmBq0/JrW68j8YCX78T9xeG9N2ybHsikbSnl9zDBlw/UIqsLB0qTz6E0dSPphzbrWkBnEVsIcQNkTaLLIb8heF8pO+qx9dbypFP/NGVbkXdFYk/093pslylOxRZp57Tgu4nbMpZgaX2T9UykLIkxuqlEWmzSHtEyohgyGWEF94+TNRcJ/MqoN3p5saFxyF7TxseSufTJIpjfe3YrXUT7IU+5So+8wRTxn6cKZ0pnIqsIxiwny07XNiG1jz7DlqRvAENV3+DwVrOOPoInU3lSLSdNapSUPVDFx4N0Q1scxkDBnrPY//6ZChc3O3Kf57FmHQRL0cjsHp0J+LzkH78mTmgzIvfb6P96HasTvTl+3AncvsFQ8H2KOni4f4L6DMH56GAOc+60FhO+DhNTLI4U1mRMvLcEGyzyKTpDwzp9n7e4U70Db2y8kcKFnQBB0tTpvFHlqsRKdjZ5yGg0C96RNu/BJAZOyLtESkjgh+XEZgHWJB4UeI/DgTITtx/HWJyMJbMAeYAc4A58LFwgEWcdw68c2AOMAeYA8yBIOUAi3iQGu5jWWVyP3lHxRxgDjAHPHOARZxFnFfgzAHmAHOAORCkHGARD1LD8cqUdyfMAeYAc4A5wCLOIs4rcOYAc4A5wBwIUg6wiAep4XgFzitw5gBzgDnAHGARZxHnFThzgDnAHGAOBCkHgkvEw2ORVduK++YAI1MYediGXbluAsLIGCNEk4FNLd0YnJACabzB8IN21OqmR0gTKhO5FKVHr+ExZfQyBzwZ6MYPWzMR4ZR5zD/1RCKhtA7tvYMU1U56lxQ8pA178nyN6ia4avUTzo5dghLK9N24LuH0+wEkOyXwsJShzG2pJaht6cS9QQOME3dQbY/UJ9b3EDlbzNPi9KiHwCnDzciKdMaGIsAt1eMcxdMfPq1F+GwGt18x9IYPhZkV4LMFZy/9EsRHFmfpPep0HPhjJtbdG+PdZwGcDb78GxZd5oCZA0Ek4ip8Xi2FZjXgVpMeX6zTo6GbYoxPdGGza2hWb8YNj0fx+VGYJnvQUKlDhrYMX18aolCnPdiRYo2UJlImLBo5LX/S+2+b60nLykfVsV5Mml7gP0XW0Kz+qkehQtyGYzhVtw0la4pQULEHp3+l1Kevf0JxrNLPg9lPODvZQJG6gzJwvcaIOxEP0yB5WwcGJwdx8/R+6MuKkJ21DFFqW78E+i5kiygszMpF1mqt/Vm1/jv88oYyg50tscfs/zRuJSoOX6bUuBYRejErEfcjhnL4iHCMbCHbr3ABfERwluwemYPjFCf+cUMxlttDxaYjIcqHXAA8Qft5XAsu4Bn3oMM9eETclujhSoUjSUr8RlxxmyTFM2FD6DddlOP5129T8amVsCELitFOAjN4LNscNlSkzBz1ShyhndpQs1P8dmtc96eNmX6tx925V8Say7SgeYJD6bMM0eppsPoJZ1ubQ2OKcKLfgN6DBSj6ccRlJ04pNnOO4cl4N3ZlzBfepc3ou4AtZmBI4XtLOmgxN3gCOdG2BUMkUuv7MPSgFd+WV+AU2XdWIu43DOXxEeKqYhb9coePKM6aQrS/mkRXaWzQTYh8xsxiH2wcCBoRd6RcTMKinTcx/rIHtWnJlnSlveLpSkMXbcNdKRf2Zqe46+GLseUu1dPzJaIoNrpImTn0G30PufX7jyJngWWHoUjbiwfGUbStjTMLkr/qmU4qcq0uKcSeG+P468kRZGr8O+j8hbO5zcqFKL84hOGOCsQq3cSNp3jd669TfHrDI/QNjmGKjjZePvsZzduyoXI6knD030PfBWwxHUMlVLpmPDeN4NxalyOJMKVlMSGb49sz7n7DUAAfIY5J/fGpXx7wEcQ5JL4KPxsNuFSVhujoSOHFWbBNntxe/849jOfs8AwoEQ9VR0O9IAaR0hMdDYXTRK7IbCIX5zha8+ORfrTfnBHq1GpKwtHxkjKS1SPVJc+yR0JIqUOfkmA/aoRuoTTBkDAkFVoym/VWI1Z6p0gZSbSXVqONdmumsXvoONOOey9f4k69Dhpbu/1Vj3XXHBr/JW6RF8GclGWiB/tWRs9qgnwvONMRQLz+Csaen0WBOaWqGxHXFKCVPCBjVw9hY8EqpGZqUdbQbc5md77MKbe8tCCS6busLZw9D8pkVN+h44j7tVgU4WHgvIWI+42rIvgIcsw+HkT65QUfEZw/S6nBtYERvLJydWrgJo5WpiLMJXkQT9qzm7QZN8bNmQMBJOLWSd6egWwYzbnWtJaSYNpFnP6mjEF8Uiw+U0T5LuJ0uSciqxYXn1myb1meCfN/JzuLzbmwpQtA8mUioMlvQu/4AC7X12BXUyt6pQxsT89h42Kbi9tf9VhJGx6NhPRVWL3+KzR2DcM0dRf7Mh0YiQ3u94NzaFwZ2kdHcKE8wZIC1o2Ih8Zvwm3JK7JlkbWMtIBajr20oDLe2ACN86Tvte8itnAMfEV6Pf4wvcKlMi8XA0XEzsORhL+4KoaPCMecJj2BfnnGxzecpQuLyiQtNp1+RGlrp6dhFeMqT9aME3NAjgMBJOIRUC3VIje/EFrzo0WS00UYh4vSaYcWnmhxp98Vd6c7AFFDnZCChUmJ0Cz7Bg9JTH7ZvsSao9lGHC9lNHk4PUJn6/9Os6ffDF1QgOO0M399cR0ip+06/FWPE6E1OjRTvvTxczp7ilU5Y9tugr97nJWYX3HLaZHkclN5qgf6RREIia3ALSmP+/7ltCCz9i0sDiXdVP5BDRKVHgawa999ssU8pH1Pub0NrdBFeZkgBMTOE97+4qpv+IjwWeSYwAs+PuHshO28XJyki26GtnwfucoTuNiYZpw+ZpwCSMRliGi7LNS5wXGxLW4jOt1ebFMhKq8GdYd3QRuv8nq5JkS9DNXX6Za7oQNrzW7fme1wV2Zu0nbco0+9Lq+LcfpNDIqvTZKbdgc+dyNA/qrH3EaNFs3DNDG2F/h3YvQTzqHzF2Nx6nIssT/ZqLxEOA+exJr0JVBL+NC7Dg/Qrvv2FsTZjiBIPBvpb68urLF6RdzwwqXvPtlCcj8/o4UW1a/2dhP3LURc6lcDvWPibbk6C3xk+SzXLy/4+ISzM7Zkr1Ms4nzJj2++vxMOBI+IK9RI2Cp9YjaOrvrNKFy7GXX/G6ez4ZvQu35iZr0lLrnIh1ucbo9bXeVhielYkbcOVd+cwNWn5FY33kdjgbNrlb6nlSujWmE5R3/6X2xfsxIpy7ORX3MO/dLOsi7Dujv3Uz3KRBTsPYjtVSXQ5Rcgr7QahzppN2n6Ey351s/Z/DZA/IWzq/i6OROnW9NJe3rJ1WrA9QOlyMrSofLkQxhN/WjKsR4TiPRdyBaW9sxN2kmLLyN69E4XG52wC1ElICktA0uzK/EjeToMF/XIoM+kliTOd7j8ZbH2F4YC+EjffstxVfriQrBfXvERwnk+0nfV4+st5cgnrupKt6LuisRVdqd/zLtF7vu785YEkYgTCOFxyN7ThofS2TNNxGN97ditdRPshT6PKT7zBFPGfpwpdZ2s5yH9+DNzwJQXv99G+9HtWJ3o+pmWSBk6p0/egIarv8FgPVs3jj5CZ1M5Eu3BTPxUjyoFVT904dEQ3eQ2v8uAgd7z2L8+GYp3cVnILziLiDiVUdLlxP0X0GcO4EO712ddaCynftmEUrDv8rawtEf5z7MYky5F5mjcrooVWUcwYL8r4TgGMLTm+ebx8BeGcvgoBDkm2C9ZfOQ4Tzfq8w53om/olZWrUmCiCzhYmvJuuCq7oHp3kycLE2MbCBwILhHnAftO3DGBQERuA0+IzAHmAHPAdw6wiPPCgBcGzAHmAHOAORCkHGARD1LD8YrV9xUrY8aYMQeYAx8aB1jEWcR5Bc4cYA4wB5gDQcoBFvEgNdyHtprk/vAOiTnAHGAO+M4BFnEWcV6BMweYA8wB5kCQcoBFPEgNxytW31esjBljxhxgDnxoHGARZxHnFThzgDnAHGAOBCkHAkfEw2ORVduK++agH1MYediGXbluArl4A3qeFqdHXeJ02wJ3DDcjK9J5FUqRrpbqcY5inQ+f1iLcbb2U4Sy1BLUtnbg3aIBx4g6qnaPDibRZnY4Df8xsU/fGeGsGskgklNahvXeQotFJ5aTgGG3Yk+clOYcnDETaI0JU2TZHYN7Kr3DixmOMmjNVTeJF30XsK0qcHnteth6bPbzZQhAf4Xf5YScSUDgL4iPSZpEydv4ooUzfjeuUhc70+wEk2wMc+QFfEY5yGRZd5oCZAwEi4ip8Xi2FVDXgVpMeX6zTo6Gb4mxPdGGza0hVb4YLj8LCrFxkrdban1Xrv8Mvb4wYOltij7n+adxKVBy+TKlNLeL6wp2Ih2mQvK0Dg5ODuHl6P/RlRcjOWoYotdI6eATbHJmD4xQ3+nFDMZZT+M6l5icdCfbkLirEbTiGU3XbULKmCAUVe3D6V0qT+fonFMfa3iUyMQq2R4T4cm1WpaLmzhB+66jDpvVfoGBDLf7vMS2+Jm+gIsEp/rxcPdQWeVsI4iPwLv+40QINZxF8RNosUsbBQ0XqDlwde40RFnEWU5E5hcu8M54EhojbEkZcqXAkN4nfiCtuk5uICJq1DIVfLekYpaQbJ5ATbRPESKTW92HoQSu+La/AKdqJzxRxSu+YcwxPxruxK2O++5zdom3WFKL91SS6SmOFjRix5jItaJ7gULprOFgvfRdtj8hgEmhziDJyWizx8NwWvDCN4qzOKTWqbD0itpjZZ7f4yL7LB954wygQcXZp7wx8RNosUsb6ntCYIpzoN6D3YAGKfhzhnbjImOIywvOffxbbfhrvQWC3gBBxR+rGJCzaeRPjL3tQm5ZsSTPaO5s0o5IBlVDpmvHcNIJza11c02FKizB7yuhE8Z/XX6c45YZH6Bscw5TpDV4++xnN27KhsmbbEm1zSHwVfjYacKkqDdHRke4XBHaikPt+SSH23BjHX0+OIFMjTkTR9ogMEN/aLLUxAtEV12jhcR81KWr7YBWqR84W0waRZ3yE3uWHARmIODts6h4fkTaLlDG/R7kQ5ReHMNxRgVilu6Q24pwV4SKXYTyZA945EBAirshsItf2OFrz45F+tN+c8ejUakqM0fGSsoTVI1U1CyIrk1F9h9zS92uxKMLD7z2JuKYAreQmHLt6CBsLViE1U4uyhm5zBrXzZZZ85qJt/iylBtcGRvDKfHZsxNTATRytTEWYS+KS0PgvcctaxjTRg30ro2UEf3qfRNsjMiBE22yrS5FSjUuUlOaP73VQO/XLp3pkUmTK4ePTu2TEPFQdDfWCGERKT3Q0FLY0qT7Y/X3iLL3LGz4i3BApM0ehQrz+Csaen0WBOW0vi7iInbnMLOZvPyy4PxbcA0zEyRWrjEF8Uiw+U0S9lYgr0uvxh+kVLpV5uSDmQThC4zfhNgnunS2LHC5j1XLspdSjxhsboCGhckx6om2OgDJJi02nH1HqTTdpGcOjkZC+CqvXf4XGrmGYpu5iX6aTa1qG1L63R2RgybSZvB0RK3bT2SjdOTivx0KPl5vk6vHiFbH1WxgfgXd5xdIqTPZMZsNoznXYIWBx9oKPSJtFyoTGlaF9dAQXyhOs44JF/GMRCu6nyHz595QJCBF3uPIsu1wzYcITLe70u7Nxp89D2veUw9jQCl2UF2A9iHhIbAVuSXnB9y+nxYT192FxKOmm9jyoQaKSdj5J23GXynRX+djmebk4SRfdDG35nlNbanRoplzW4+d0wukvZ90ekRWv2zZHQJ1zAD10b6H/zAbEi3hLvPVdZic+bRIRwUcEZ7d9j4BqqRa5+YXQmh8tkuyXEN/C7u8TZxd8RLghX0aJ+RW3rOlF3XwBMtUD/SKnS40i/eUyfE7MHHhrDgSEiM+xXarp3OC42Ba3EZ1uL7apEJVXg7rDu6CNV7kHgOqrf2bE6wtroPZGEk/CQb8/PEC77ttbEGdzpVLZRvrbK6pTJdXpU5udFhIaLU7JirgWzcMk9O0FwiLuW3sEMHTGzU2b5yZW4cK4Ec/OlGEBLWqEVure+u6TiAvgI4LzbCYQn+z+9+E8jT8ibRYoEzp/MRanLscS+5ONykv0FcngSaxJXwK1KA9mgzv/RmyMMU4fHU6BIeIKNRK2Sp+YjaOrfjMK125G3f/G6ROzm9C7fmIWudq8k5XOl4dbchHmhrRzk3biHv17jz5x2g1qm9CEqBKQlEafemVX4kfa8Rou6pFBn34tSZxvLR+JpD295PY24PqBUmRl6VB58iGMpn405dhcqyJtno/0XfX4eks58vMLoCvdiror5CFwdqcrE1Gw9yC2V5VAR2XySqtxqFMq8yda8mN8OBcXaY9VbL1iKNBmOgvNOvWcPB0/YVPOCutnc5bP51KWxFi/FRepJxyythDCR+xdQgsN2UkwwHAWwkekzSJlXBdr7E73D6cEF8Gy3OR6PkZ7BIiIS+7zOGTvacNDOl+VBHqsrx27tW6CvdBnY8VnnmDK2I8zpe5FWvnPsxiTLsflaNyuyhRZRzBgP/N0uAYNrXmOna+SLtbtv4A+c/AZIyaedaGxPBkK54Ek12a65Z53uBN9Q6+sbkgpkMsFHCxNgcJ2AUyVgqofuvBoiG7Dm9tkwEDveexfT+9yufwmS1C59tiPKrxgKNLmiBTs7PMQVOcXPaIl74VIPdJFMTlbiOAj+C5Z/EQnyUDCWQQf8/GUwPgSKTMNIxZxv3FKlHtc7qPbactxLHBEnMnJ5GQOMAeYA8wB5oBPHGARZ8L4RBi5VSH/O7s0mQPMAebA++MAiziLOIs4c4A5EEQcsBy7iT0spu9PTP8urFnEg2jw/l0k4fd++BMB2zh4bCwJuIi9RMuJ1MVlApcfLOIs4kITAg/iwB3EbJuPyzai4ixajvkT3PxhEWcRZxFnDjAHgogDouIsWo5FnEWcJ4AgmgB4wAb3gH1n9guPQcaOYzj5bTbCg4XP4QuQtq0Jx77Ochsv4p1h5Qs+6n9g43fHsa/IKbKjL793U1ZUnEXLBQROb4nJx9yHt9qJhyZU447zBYv/lXqPkBYAhgqGNn+27AAe0/fi59f6EuyFxcl5IM8Gw0DjxmzbM5u+z6GEQTse0mWpAB7DM/rl1zYrEZl/Eo/fvMLdQ5nT40HQvDVbW8yJKkEXzZEPd6fgU5f5L6LoEibeXEOht9DQLOK8SZTRzf8Hob4WQpplOZ8AAAAASUVORK5CYII=" alt="" width="497" />
  • 字段相加就会使用到该辅助函数，那么输出结果如下：
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" alt="" width="496" />
 
（4）class  pcl::FeatureHistogram
用于计算一些浮点数均值和方差的直方图类型。
GlobalDescriptorsPtr global_descriptor;

global_descriptor = computeGlobalDescriptor (cloud, normals);

pcl::visualization::PCLHistogramVisualizer hist_vis;

hist_vis.addFeatureHistogram (*global_descriptor, 308, "Global descriptor");

　　

（5）class   pcl::GaussianKernel
高斯核类集合了所有使用高斯核计算、卷积、平滑、梯度计算图像的方法。
float* kernel_ptr_host;

int kernel_size = 5;

float sigma = 1.0;

kernel_ptr_host = probability_processor_->CreateGaussianKernel(sigma, kernel_size);

　　

（6）class  pcl::PCA< PointT >
主成分分析（PCA）类。
通过对输入点云集中心点的协方差矩阵进行奇异值分解，提取主成分。pca计算后的可用数据有输入数据的平均值、特征值（降序）和相应的特征向量。
其他方法允许在特征空间中进行投影、从特征空间重构以及用新的数据更新特征空间(根据Matej Artec, Matjaz Jogan and Ales Leonardis: "Incremental PCA for On-line Visual Learning and Recognition”)。
pcl::PCA<pcl::PointXYZ> pca;

pcl::PointXYZ projected, reconstructed;

for(size_t i = ; i < cloud.size(); i++)

  {

    pca.project (cloud[i], projected);

    pca.reconstruct (projected, reconstructed);

  }
 
（7）class   pcl::PiecewiseLinearFunction
提供了返回有效分段线性函数的值的功能。
inline float PiecewiseLinearFunction::getValue(float point) const

{

  float vector_pos = factor_*point + offset_;

  float floored_vector_pos = floor(vector_pos);

  float interpolation_size = vector_pos-floored_vector_pos;

  int data_point_before = (std::max)(, (std::min)(int(data_points_.size())-, int(lrint(floored_vector_pos))));

  //cout << "Interpolating between "<<data_point_before<<" and "<<data_point_before+1<<" with value "

       //<< interpolation_size<<" (vector size is "<<data_points_.size()<<").\n";

  return data_points_[data_point_before]+interpolation_size*(data_points_[data_point_before+]-data_points_[data_point_before]);

}
（8）class  pcl::PolynomialCalculationsT< real >
为多项式提供了一些功能，如寻找根或逼近二元多项式。
PolynomialCalculationsT<RealForPolynomial> polynomial_calculations;

BivariatePolynomialT<RealForPolynomial> polynomial ();

float interest_value2 = interest_image_[index2];

sample_points.push_back (Eigen::Vector3d (x2-keypoint_x_int, y2-keypoint_y_int, interest_value2));

polynomial_calculations.bivariatePolynomialApproximation (sample_points, , polynomial)
（9）class  pcl::PosesFromMatches
基于点对应的关系计算他们之间的三维空间变换
 
（10）class   pcl::StopWatch
一个用于计时的秒表。
 // Time measurements

  pcl::StopWatch sw;

  pcl::StopWatch sw_total;

  double t_select     = .;

  double t_build      = .;

  double t_nn_search  = .;

  double t_calc_trafo = .;

    ....

  sw.reset ();

  ...

  t_select = sw.getTime ();

  sw.reset ();

  kd_tree_->setInputCloud (cloud_model_selected);

  t_build = sw.getTime ();

  ...

  t_nn_search += sw.getTime ();
（11）class   pcl::ScopeTime
测量作用在作用域中的时间。
要使用这个类，例如测量函数中所花费的时间，只需在函数的开头创建一个实例。例子
{

pcl::ScopeTime t1 ("calculation");

// ... perform calculation here

}
（12）class   pcl::EventFrequency
 一个辅助类来测量某个事件的频率。
 
（13）class   pcl::TimeTrigger
 定时调用回调函数的计时器类。
 
（14）class  pcl::TransformationFromCorrespondences
基于对应的3D点计算变换。
 pcl::TransformationFromCorrespondences transformation_from_correspondeces;

transformation_from_correspondeces.reset ();

      transformation_from_correspondeces.add (corr1, point1);

      transformation_from_correspondeces.add (corr2, point2);

      transformation_from_correspondeces.add (corr3, point3);

      transformation_from_correspondeces.add (corr4, point4);

      transformation_from_correspondeces.add (corr5, point5);

      transformation_from_correspondeces.add (corr6, point6);

      transformation_from_correspondeces.add (corr7, point7);

      transformation_from_correspondeces.add (corr8, point8);

      ++counter_for_added_pose_estimates;

      PoseEstimate pose_estimate;

      pose_estimate.transformation = transformation_from_correspondeces.getTransformation ();

      pose_estimate.score = 0.5f * (correspondence1.distance + correspondence2.distance);

      // TODO: base on the measured distance_errors?

      pose_estimate.correspondence_indices.push_back (correspondence1_idx);

      pose_estimate.correspondence_indices.push_back (correspondence2_idx);

      pose_estimates.push_back (pose_estimate);
（15）class  pcl::VectorAverage< real, dimension >
计算给定权重的一组向量的加权平均和协方差矩阵的类
VectorAverage3f vector_average;

float max_dist_squared=max_dist*max_dist, max_dist_reciprocal=1.0f/max_dist;

  bool still_in_range = true;

  for (int radius=;  still_in_range;  ++radius)

  {

    int x2=x-radius-, y2=y-radius;  // Top left - 1

    still_in_range = false;

    for (int i=; i<*radius; ++i)

    {

      if (i<=*radius) ++x2; else if (i<=*radius) ++y2; else if (i<=*radius) --x2; else --y2;

      if (!isValid (x2, y2))

      {

        continue;

      }

      getPoint (x2, y2, neighbor);

      float distance_squared = (neighbor-point).squaredNorm ();

      if (distance_squared > max_dist_squared)

      {

        continue;

      }

      still_in_range = true;

      float distance = std::sqrt (distance_squared),

            weight = distance*max_dist_reciprocal;

      vector_average.add (neighbor, weight);

    }

  }
（16）struct  pcl::Correspondence
对应表示两个实体（例如，点、描述子等）之间的匹配。通过源点云与目标点云之间的距离来表示。
//  Find Model-Scene Correspondences with KdTree

  //

  pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());

  pcl::KdTreeFLANN<DescriptorType> match_search;

  match_search.setInputCloud (model_descriptors);

  //  For each scene keypoint descriptor, find nearest neighbor into the model keypoints descriptor cloud and add it to the correspondences vector.

  for (size_t i = ; i < scene_descriptors->size (); ++i)

  {

    std::vector<int> neigh_indices ();

    std::vector<float> neigh_sqr_dists ();

    if (!pcl_isfinite (scene_descriptors->at (i).descriptor[])) //skipping NaNs

    {

      continue;

    }

    int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), , neigh_indices, neigh_sqr_dists);

    if(found_neighs ==  && neigh_sqr_dists[] < 0.25f) //  add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)

    {

      pcl::Correspondence corr (neigh_indices[], static_cast<int> (i), neigh_sqr_dists[]);

      model_scene_corrs->push_back (corr);

    }

  }

  std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;
（17）struct   pcl::PointCorrespondence3D
表示两个不同坐标系中的两个3D点之间的(可能的)对应关系(例如，来自特征匹配)
（18）struct  pcl::PointCorrespondence6D
表示两个点之间的（可能的）对应关系（例如来自特征匹配），是一个6DOF变换。
 
这三个类是继承的关系。
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（19）以下是点云库中已经定义好的点云的形式
 
struct  pcl::PointXYZ
表示Euclidean xyz坐标的点集结构类型
struct  pcl::Intensity
表示单通道图像灰度强度的点集结构类型
struct  pcl::Intensity8u
一种表示单通道图像灰度强度的点集结构类型
struct  pcl::Intensity32u
表示单通道图像灰度强度的点集结构类型
struct  pcl::_PointXYZI
表示欧氏XYZ坐标的点集结构和强度值
struct   pcl::PointXYZRGBA
表示欧氏XYZ坐标和RGBA颜色的点集结构类型
struct  pcl::PointXYZRGB
表示欧氏XYZ坐标和RGB颜色的点集结构类型struct  
pcl::PointXY
表示Euclidean xy坐标的二维点集结构类型
struct  pcl::PointUV
表示像素图像坐标的2D点集结构类型
struct  pcl::InterestPoint
表示具有欧几里德xyz坐标和兴趣值的点集结构类型
给个例子关于这个特殊的类型：
pcl::visualization::RangeImageVisualizer*

pcl::visualization::RangeImageVisualizer::getInterestPointsWidget (

    const pcl::RangeImage& range_image, const float* interest_image, float min_value, float max_value,

    const pcl::PointCloud<pcl::InterestPoint>& interest_points, const std::string& name)

{

  RangeImageVisualizer* widget = new RangeImageVisualizer;

  widget->showFloatImage (interest_image, range_image.width, range_image.height, min_value, max_value);

  widget->setWindowTitle (name);

  for (unsigned int i=; i<interest_points.points.size(); ++i)

  {

    const pcl::InterestPoint& interest_point = interest_points.points[i];

    float image_x, image_y;

    range_image.getImagePoint (interest_point.x, interest_point.y, interest_point.z, image_x, image_y);

    widget->markPoint (static_cast<size_t> (image_x), static_cast<size_t> (image_y), green_color, red_color);

  }

  return widget;

}
struct  pcl::Normal
表示法向量坐标和曲面曲率估计的点集结构类型
struct  pcl::Axis
用法向量坐标表示轴的点集结构
struct  pcl::PointNormal
表示欧几里德xyz坐标的点集结构，连同法线坐标和表面曲率估计值
struct  pcl::PointXYZRGBNormal
表示欧几里德xyz坐标和RGB颜色的点集结构，以及法线坐标和表面曲率估计.
struct  pcl::PointXYZINormal
表示欧几里德xyz坐标，强度，连同法线坐标和表面曲率估计的点集结构类型。
struct  pcl::PointXYZLNormal
表示欧几里得xyz坐标，一个标签，法线坐标和表面曲率估计的点集结构类型
struct  pcl::PointWithRange
表示欧几里德XYZ坐标的点集结构，并连同的浮点数的深度信息
struct  pcl::PointWithViewpoint
表示欧几里得xyz坐标的点集结构以及的视点的点集结构
struct  pcl::MomentInvariants
表示三个矩是不变量的点集结构类型
struct  pcl::PrincipalRadiiRSD
表示使用RSD计算的最小和最大表面半径（以米为单位）的点集结构类型
struct  pcl::Boundary
表示点是否位于表面边界的点集结构
struct  pcl::PrincipalCurvatures
表示主曲率及其幅值的点集结构
 
（20）以下是一些三维特征点描述子的点集结构，其中每个描述子都是一篇论文，希望有兴趣的小伙伴加入我们，每个人分析解释一种描述子的由来以及理论研究。
 
struct  pcl::PFHSignature125       
表示点云的特征直方图（PFH）的点集结构类型
struct  pcl::PFHRGBSignature250     
表示颜色特征点特征直方图的点结构（PFHGB）
struct  pcl::PPFSignature
用于存储点对特征（PPF）值的点集结构
struct  pcl::CPPFSignature
用于存储点对特征（CPPP）值的点集结构
struct  pcl::PPFRGBSignature
用于存储点对颜色特征（PPFRGB）值的点集结构
struct  pcl::NormalBasedSignature12
表示4-By3的特征矩阵的基于正常的签名的点结构
struct  pcl::ShapeContext1980           
表示形状上下文的点结构
struct  pcl::UniqueShapeContext1960   
表示唯一形状上下文的点结构
struct  pcl::SHOT352           
表示OrienTations直方图（SHOT）的通用标签形状的点集结构
struct   pcl::SHOT1344     
一种点结构，表示OrienTations直方图（SHOT）的通用签名-形状+颜色。
struct  pcl::_ReferenceFrame
表示点的局部参照系的结构
struct  pcl::FPFHSignature33     
表示快速点特征直方图（FPFH）的点结构
struct  pcl::VFHSignature308     
表示视点特征直方图（VFH）的点结构
struct   pcl::GRSDSignature21   
表示全局半径的表面描述符（GRSD）的点结构。
struct   pcl::BRISKSignature512    
表示二进制鲁棒不变可缩放关键点（BRISK）的点结构。
struct  pcl::ESFSignature640      
表示形状函数集合的点结构（ESF）
struct  pcl::GASDSignature512       
表示全局对准的空间分布（GASD）形状描述符的点结构
struct   pcl::GASDSignature984    
表示全局对齐空间分布（GASD）形状和颜色描述符的点结构
struct   pcl::GASDSignature7992          
表示全局对齐空间分布（GASD）形状和颜色描述符的点结构
struct  pcl::GFPFHSignature16         
表示具有16个容器的GFPFH描述符的点结构。
struct  pcl::Narf36       
表示NARF描述符的点结构
struct  pcl::BorderDescription
用于存储距离图像中的点位于障碍物和背景之间的边界上的结构
struct  pcl::IntensityGradient
表示Xyz点云强度梯度的点结构
struct  pcl::Histogram< N >
表示N-D直方图的点结构
struct  pcl::PointWithScale
表示三维位置和尺度的点结构
struct  pcl::PointSurfel
曲面，即表示欧几里德xyz坐标的点结构，连同法向坐标、RGBA颜色、半径、置信值和表面曲率估计。
struct  pcl::PointDEM
表示数字高程图的点结构 Digital Elevation Map. ..
class  pcl::PCLBase< PointT >
PCL的基类
struct  pcl::GradientXY
表示欧氏XYZ坐标的点结构和强度值
 
 
 对于以上描述子的具体解读，我们已经组织群友们，一起阅读，并写出自己的理解，希望大家一起能加入我们学习分享。

有兴趣的小伙伴可以关注微信公众号，加入QQ或者微信群，和大家一起交流分享吧

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