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

TL;DR

Multi-FEAT is a novel targetless calibration method that aligns camera and LiDAR sensors using edge features and panoramic projections, improving accuracy over existing methods.

Contribution

It introduces a new targetless calibration approach leveraging panoramic projections and edge features, enhancing reliability and accuracy in sensor alignment.

Findings

Multi-FEAT outperforms existing targetless calibration methods on KITTI dataset.

The approach effectively uses panoramic images to encode 3D LiDAR data.

Edge-based feature matching improves calibration robustness.

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.