In recent years, many excellent SLAM methods based on cameras, especially the camera-IMU fusion (VIO), have emerged, which has greatly improved the accuracy and robustness of SLAM. However, we find through experiments that most of the existing VIO methods perform well on drones or drone datasets, but for ground robots on complex terrain, they cannot continuously provide accurate and robust localization results. Some researchers have proposed methods for ground robots, but most of them have limited applications due to the assumption of plane motion. Therefore, this paper proposes GR-SLAM for the localization of ground robots on complex terrain, which can fuse camera, IMU, and encoder data in a tightly coupled scheme to provide accurate and robust state estimation for robots. First, an odometer increment model is proposed, which can fuse the encoder and IMU data to calculate the robot pose increment on manifold, and calculate the frame constraints through the pre-integrated increment. Then we propose an evaluation algorithm for multi-sensor measurements, which can detect abnormal data and adjust its optimization weight. Finally, we implement a complete factor graph optimization framework based on sliding window, which can tightly couple camera, IMU, and encoder data to perform state estimation. Extensive experiments are conducted based on a real ground robot and the results show that GR-SLAM can provide accurate and robust state estimation for ground robots.

This study is published in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).