GR-LO: A specific lidar odometry system optimized with ground and road edges

Abstract

For the specific environment, we propose a specific lidar odometry system optimized with ground and road edges, GR-LO, for unmanned ground vehicle (UGV) navigation in the Global Positioning System (GPS) coordinate system. Many high-performance lidar odometry (LO) and lidar-based simultaneous localization and mapping (SLAM) systems have been proposed. However, when we need to convert the pose of lidar from the lidar coordinate to the GPS coordinate system for navigation, there is usually a nonnegligible error. One of the reasons for the error is that the transformation between the two coordinate systems is not calibrated correctly. Our method is proposed to reduce this error by extracting prior surrounding features in the specific environment. We have tested the proposed method with our datasets. The results show that by utilizing the prior features, the proposed method can reduce the absolute pose errors by more than 50% compared to the adopted lidar odometry method.