🤖 AI Summary
This work addresses the boundary blurring and distortion in LiDAR–camera extrinsic calibration caused by laser beam footprint effects and mixed-intensity returns. To this end, the authors propose a joint calibration method that integrates boundary response modeling. By co-observing visual fiducials on a printable planar calibration board and LiDAR-visible circular reflective boundaries, the approach iteratively refines 3D LiDAR edge feature points. It further incorporates an intensity- and geometry-constrained refinement strategy and a confidence-weighted reprojection optimization framework. Notably, this is the first method to embed explicit modeling of LiDAR boundary response characteristics into the extrinsic calibration pipeline. Evaluated on real-world data, it achieves sub-pixel reprojection accuracy and millimeter-level feature consistency, substantially improving downstream visual–LiDAR odometry performance.
📝 Abstract
We present LV-Calib, a calibration framework for LiDAR-camera extrinsic estimation and LiDAR boundary-response calibration using a printable planar target. The target serves as a shared observation carrier: visual fiducials provide indexed image measurements, while circular reflectivity boundaries provide LiDAR-observable structural feature points. Instead of directly fitting boundary points as ideal geometric contours, LV-Calib automatically crops background points, estimates the target plane, and iteratively refines accurate LiDAR-side 3-D feature points from intensity and geometric constraints. The refinement explicitly handles the broadened and distorted transition band induced by finite beam footprint and mixed-intensity returns around black-white reflectivity discontinuities. Given these refined LiDAR features, we formulate a weighted reprojection-consistent extrinsic optimization with LiDAR feature alignment, where image observations are kept in the reprojection domain and LiDAR feature residuals are weighted by refinement confidence. Finally, using the estimated extrinsic and the extracted transition band, LV-Calib calibrates the LiDAR boundary response by estimating pitch-yaw-range residual statistics of boundary-overlap samples. Experiments on printed-board calibration data demonstrate sub-pixel reprojection accuracy, millimeter-level LiDAR feature consistency, and improved odometry performance. Code and calibration data will be released for reproducible evaluation.