Tightly Coupled 3D Lidar Inertial SLAM for Ground Robot

Abstract

This paper proposes a robotic state estimation and map construction method. The traditional lidar SLAM methods are affected by sensor measurement noise, which causes the estimated trajectory to drift, especially along the altitude direction caused by lidar noise. In this paper, ground parameters in the environment are extracted to construct the ground factors to compress the trajectory estimation drifting along the altitude direction using the characteristics of constant robot pose relative to the ground. Our method uses tightly coupled lidar and inertial to obtain low-drift lidar odometry factors by factor graph optimization. The optimized lidar odometry factors are then added to a global factor graph, together with ground, loop closure, and GPS factors to obtain accurate robot state estimation and mapping after factor graph optimization. The experimental results show that our method has comparable results with advanced lidar SLAM methods, and even performs better in some complex and large-scale environments.

Fast Lidar Inertial Odometry and Mapping for Mobile Robot SE(2) Navigation

“…Traditional Lidar SLAM methods are corrupted by noise, and the estimated trajectories have drift issues in the z axis. Ground parameters have been extracted to build ground factors in order to eliminate the drift in the altitude direction [25].…”

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

“…Hence, this study introduces a fusion structure that integrates LIO-SAM with GPS information. While LIO-SAM-based approaches have included GPS information in their factor graph [6,7], it is difficult to utilize covariance appropriately. In contrast, the proposed approach employs covariance intersection (CI) as a covariance-based convergence technique to combine the two sets of information in the back-end stage.…”