LTI-SAM: Lidar-Template Matching-Inertial Odometry via Smoothing and Mapping

Abstract

Robust localization is an essential requirement for autonomous land vehicles. In similar scenes such as corridors and tunnels or open scenes such as airports and mines, the performance of the traditional algorithm of laser odometry will be seriously deteriorated or even become completely invalid due to the similarity of observation data. Based on the LIO-SAM algorithm, LTI-SAM is proposed in this article, that is to use the downward-facing camera and inertial measurement unit (IMU) to perform dead reckoning through template matching algorithm and use template matching visual odometry (VO) to judge themotion state and decidewhether to save the keyframe if the laser odometer fails and the keyframe is not saved. If the keyframe is kept, theVOis added between two keyframes instead of the laser odometry to optimize through the factor graph, which improves the accuracy of localization and mapping. The proposed algorithm with loose coupling between template matching VO and laser inertial navigation is implemented under the robot operating system (ROS) platform, and the algorithm is verified by indoor and outdoor experiments. Experimental results show that the proposed algorithm can bring significant performance improvement in both similar indoor scenes and outdoor degraded scenes.