VeriCalib: Verifiable Target-Based LiDAR-Camera Calibration

Key points

• 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.1
• 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.1
• 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.1
• On this covariance, we decouple rotation and translation to resolve the precision along each direction and guide data collection toward the weakest one.1

Sources (1)

• [1]VeriCalib: Verifiable Target-Based LiDAR-Camera Calibration
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 06:21 PM

  This paper presents VeriCalib, which formulates extrinsic calibration as a verifiable estimation problem and reports a calibration state.

  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.

Extractive summary: sentences quoted from the sources.