基于惯性测量单元辅助的激光里程计求解方法

在同步定位与建图(SLAM)问题中,里程计部分的求解精度对后续建图起着至关重要的作用,惯性测量单元(IMU)可以为SLAM中里程计求解提供良好辅助。在考虑平面移动机器人运动特点及室内环境特征的基础上提出一种基于IMU松耦合的激光里程计求解方法,实现里程计部分的精准定位。第1阶段,机器人运动过程中实时处理点云信息,将地面点分割并提取有效关键点;第2阶段,将IMU信息引入卡尔曼滤波器,为帧间匹配提供位姿先验;第3阶段,滤波器输出位姿估计值后,利用非线性优化方法进行点云帧间匹配,实现里程计运动的求解。实验表明,所提方法在激光点云处理、运动求解,具有良好的稳定性和准确性,可将偏移量误差控制在0.4%以内,为后续建图提供有力数据保障。

Solving method of lidar odometry based on IMU

In the simultaneous localization and mapping(SLAM) problem, the solution accuracy of the odometry part plays a vital role in the subsequent mapping. The inertial measurement unit(IMU) can provide valuable assistance for odometry in SLAM. Based on the consideration of the movement characteristics of the planar mobile robot and the indoor environment characteristics, proposes a laser odometry solution method based on IMU loose coupling to realize the precise positioning of the odometry part. In the first stage, the point cloud information is processed in real time during the robot movement. The ground points are segmented and key points are extracted. In the second stage, the IMU information is introduced into the Kalman filter to provide the pose prior for the inter-frame matching. In the third stage, after the filter outputs the pose estimation value, the non-linear optimization method is used to match the point cloud frames to realize the solution of the odometer movement. Experimental results show that the proposed method has good stability and accuracy in laser point cloud processing and motion solving. The offset error can be controlled within 0.4%. This method provides powerful data guarantee for subsequent mapping.