Multi-FEAT: Multi-Feature Edge Alignment for Targetless Camera-LiDAR Calibration

Abstract

Multi-agent systems, e.g., automobiles and UAVs (Unmanned Ariel Vehicles), rely on the precision of onboard sensors to accurately perceive their environment, which in turn depends on the precision of onboard sensors and reliable in-field calibration. This paper introduces a novel targetless camera-LiDAR extrinsic calibration approach called Multi-FEAT (Multi-Feature Edge AlignmenT). Multi-FEAT uses the cylindrical projection model to encode the 3D LiDAR point cloud into a 2D panorama and exploits diverse LiDAR feature information in panoramic images to supplement the sparse LiDAR point cloud boundaries. Furthermore, camera edges are extracted using off-the-shelf segmentation solutions. In addition, a feature-matching function is designed to optimize the calibration parameters. The performance of the proposed Multi-FEAT algorithm is evaluated using the KITTI dataset, and our approach shows more reliable results than several existing targetless calibration methods. We conclude our analysis with directions for future work.