Dual-Constraint Registration LiDAR SLAM Based on Grid Maps Enhancement in Off-Road Environment

Abstract

Aiming at the influence of fewer feature points and dynamic obstacles on location and mapping in off-road environments, we propose a dual-constraint LiDAR-based Simultaneous Localization and Mapping (SLAM) scheme. By abstracting LiDAR registration into two constraints, namely, in-window constraints and out-of-window constraints, the in-window constraints allow our scheme to compromise between accuracy and real-time performance, and out-of-window constraints can exploit optimized variables to provide richer constraint information. The advantages of incremental SLAM map construction can be used to design a variety of map forms. Although the variables outside the window are no longer involved in the optimization, we can use the two-dimensional probability grid map to provide binary semantic information and dynamic weights for the constraints outside the window to enhance the registration accuracy. Finally, we conducted experiments in off-road environment and compared with the mainstream LiDAR SLAM scheme, which can prove that our scheme has more advantages in accuracy.