Improving lidar camera calibration accuracy with pattern priors
P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration
Robotics
Summary
Calibrating a camera and a lidar sensor together is important for robots to combine their data accurately. The usual method struggles because it’s hard to precisely locate points on a special target board in the lidar data. The authors introduced P²Calib, a tool that uses known shapes and distances on the target board to improve where these points are found, leading to much better calibration results. Their method was tested both on simulated and real data, showing large improvements over previous approaches.
What this means in practice
- •For robotics engineers: Perform more accurate sensor alignment for robots by using pattern-based constraints to improve lidar-camera calibration.
- •For autonomous vehicle developers: Enhance the quality of multi-sensor fusion in self-driving cars by reducing calibration errors between lidar and cameras using geometric pattern priors.
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.