Improving robot sensor alignment using pattern shapes and geometry
P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration
Robotics
Summary
Robots often need to combine information from cameras and laser sensors, but this requires precise matching of their positions. A common way to do this is by using a special target with four holes, but finding the exact centers of these holes with laser data is tricky due to sparse points and noise. The authors improved this process by using known details about the holes’ sizes and arrangement to guide the alignment. Their method significantly reduced errors in matching the sensors, making the calibration more accurate.
LiDARcamera calibrationextrinsic calibrationpattern priorssensor fusionCAD modelgeometric constraintshole-center extraction
Authors
Xiangcheng Hu
Abstract
Target-based LiDAR-camera extrinsic calibration is a prerequisite for multi-sensor fusion in robotics. However, in the widely adopted four-hole pipeline, calibration accuracy is bottlenecked by LiDAR-side hole-center extraction, which suffers from sparse angular coverage and mixed-pixel corruption. This paper presents P$^2$Calib, which exploits pattern priors, geometric constraints specified by the CAD model of the target board, to improve calibration accuracy. First, we incorporate the known hole radius as a fitting constraint to prevent center estimates from degrading under sparse angular coverage. Building on the improved hole estimates, we further enforce the rigid rectangular layout of the four holes as a global consistency constraint to correct residual errors across holes. Both priors are integrated into an interactive calibration tool that provides a complete extrinsic calibration pipeline. Experiments on simulated and real datasets show that P$^2$Calib lowers the joint registration residual by 90\% and 82\% and the held-out reprojection error by 96\% and 77\% over the baseline. Code, https://github.com/JokerJohn/P2Calib.git, and data will be released to facilitate future research.