Efficient LiDAR/inertial-based localization with prior map for autonomous robots

Abstract

A rapid and accurate localization scheme is significant for the application of autonomous robots in a prior map. However, this task remains challenging in the real-time requirement due to the complex scan matching. This paper proposes an efficient LiDAR/inertial-based localization method that simplifies the process of scan matching. Firstly, it constructs KD-tree architectures for the prior map in advance and selects sparse point cloud as local map through a novel refined neighborhood search. Then, to ensure the reliability of localization, this method removes the dynamic points in the prior map by the comparison between newly laser scan and the local map. The pose transformation is calculated by the scan matching of edge and planar points from static objects. Finally, this method introduces a uniform motion model to correct the wrong initial guess from incorrect inertial data pre-integration. Three prior maps are collected from typical scenarios through intelligent inspection robot to verify the robustness of proposed method. Experimental results show that the proposed method not only achieves high accuracy of centimeter-level deviation in localization, but takes less than 0.01 s to complete the pose matching when the LiDAR rate is 20 Hz.