Range-Aided LiDAR-Inertial Multi-Vehicle Localization and Mapping in Degenerate Environments
Zhe Jin, Chaoyang Jiang
Abstract
This paper presents a range-aided LiDAR-inertial multi-vehicle localization and mapping system (RaLI-Multi). Firstly, we propose a LiDAR-inertial odometry with multi-metric weights by fusing observations from an inertial measurement unit (IMU) and a light detection and ranging sensor (LiDAR). The degenerate level and direction are evaluated by analyzing the distribution of normal vectors of feature point clouds and are used to activate the degeneration correction module in which range measurements correct the pose estimation from the degeneration direction. We then design a multi-vehicle SLAM system in which a centralized vehicle receives local maps of each vehicle and the range measurements between vehicles to optimize a global pose graph. The global map is broadcast to other vehicles for localization and mapping updates, and the centralized vehicle is dynamically fungible. Finally, we provide three experiments to verify the effectiveness of the proposed RaLI-Multi. The results show its superiority in degenerate environments.