Verifiable calibration ensures reliable LiDAR and camera alignment
VeriCalib: Verifiable Target-Based LiDAR-Camera Calibration
Robotics
Summary
Calibrating sensors like cameras and LiDAR is crucial for many technologies, but current methods can’t always tell if the calibration is trustworthy or if more data is needed. The authors propose VeriCalib, a new way to check if the calibration correctly fits the sensor data and if it’s precise in all directions. Their method also suggests where to gather more data for better results and detects the smallest errors that can be found. This helps users know whether to accept the calibration or improve it with new data.
What this means in practice
- •For autonomous vehicle engineers: Improve LiDAR-camera calibration reliability by verifying calibration quality and guiding additional data collection.
- •For robotics system integrators: Assess sensor calibration confidence and identify weak precision directions to enhance robot perception accuracy.
Authors
Xiangcheng Hu, Xieyuanli Chen, Feiyi Chen, Shengli Ren, Shiyang Chen, Guoqiang Ye, Hao Wang, Ping Tan, Steven L. Waslander, Fuchun Sun, Jin Wu
Abstract
Target-based extrinsic calibration estimates the rigid transform between sensors from a few observations of a known target, and its result is commonly assessed by the root-mean-square error of the fitted residuals. However, this scalar error is computed on the same observations and therefore cannot indicate whether to accept the result, re-extract, recollect or add observations. This paper presents VeriCalib, which formulates extrinsic calibration as a verifiable estimation problem and reports a calibration state. The key insight is a two-level extrinsic compensation mechanism, in which the extrinsic compensates the rigid part of each observation error at the residual level, and rotation and translation compensate each other at the parameter level. Consequently, the fitted residual can reveal neither an inconsistency between the observations and the model nor whether the estimated extrinsic is reliable in every direction. Accordingly, we assess the calibration through a structured covariance that jointly propagates the uncertainty of the camera and LiDAR board features with a known-geometry prior, and estimate its scale as a variance component from the residuals. On this covariance, we decouple rotation and translation to resolve the precision along each direction and guide data collection toward the weakest one. Since this precision presumes consistent observations, we further develop within-view and cross-view checks that examine the internal residual and the rigid discrepancy between observations, respectively, with thresholds that hold their significance level under an unknown noise level. We also extend the minimal detectable bias to the extrinsic to bound the smallest detectable fault along each direction. These criteria form a calibration state that indicates whether to accept the result or how to collect the data...