Investigating the Complementary Use of Radar and LIDAR for Positioning Applications

Abstract

In the realm of Autonomous Vehicles (AVs), accurate, reliable and uninterrupted positioning capabilities are vital to ensure successful operations. Light Detection And Ranging (LiDAR) technology, capable of providing a high-fidelity 3D representation of the surrounding environment, has enabled numerous odometry-based positioning algorithms. These algorithms utilize a registration process to estimate relative motion from two successive 3D scans. However, the accuracy of the registration process can be compromised by the presence of dynamic objects, leading to significant translational and rotational deviations. On the other hand, Radar technology provides spatial and speed information. However, it is limited by spatial sparsity and susceptibility to noise. In this paper, we propose combining the complementary LiDAR and Electronic Scanning Radar (ESR) measurements, along with onboard motion sensors for improved navigation performance in complex and dynamic environments. This is achieved by employing a radar-based filtering mechanism that refines the LiDAR’s point cloud mitigating the impact of dynamic objects. This results in a more robust registration process, which in turn enhances the LiDAR Inertial Odometry (LIO) solution. The proposed method was verified using real data collected from onboard motion sensors, a 3D LiDAR, and four ESRs from road tests conducted in downtown Calgary, Alberta, Canada. Our approach achieved an improved average horizontal positioning and heading RMSE of 0.43 meters and 0.25 degrees, respectively, compared to the 0.66 meters and 0.39 degrees observed with the standalone LIO solution. Moreover, submeter-level and lane-level accuracies were enhanced to 95% and 100% of the time, respectively, up from 85.7% and 94.9%.