泡泡一分钟：Tightly-Coupled Aided Inertial Navigation with Point and Plane Features

Tightly-Coupled Aided Inertial Navigation with Point and Plane Features

具有点和平面特征的紧密耦合辅助惯性导航

Yulin Yang∗, Patrick Geneva††, Xingxing Zuo†, Kevin Eckenhoff∗, Yong Liu†, and Guoquan Huang∗

This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.

本文提出了一种具有点和平面特征的紧密耦合辅助惯性导航系统（INS），一种适用于任何视觉和深度传感器（例如RGBD，LiDAR）配置的通用传感器融合框架，其中相机用于点特征 跟踪和深度传感器用于平面提取。所提出的系统利用环境的几何结构（平面），并采用最接近点（CP）进行平面参数化。此外，我们将平面点特征与非平面点特征区分开，以强制执行在我们的状态估计器中使用的点对平面约束，从而进一步利用环境中的结构信息。我们还为基于特征的同时定位和建图（SLAM）引入了一种简单而有效的平面特征初始化算法。 另外，由于很难以高精度获得此关键校准参数，因此我们在IMU和深度传感器之间执行在线空间校准。 蒙特卡洛模拟和真实世界的实验都可以验证所提出的方法。