Analytic Plane Covariances Construction for Precise Planarity-based Extrinsic Calibration of Camera and LiDAR

Gunhee Koo, Jaehyeon Kang, Bumchul Jang, Nakju Doh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Planarity of checkerboards is a widely used feature for extrinsic calibration of camera and LiDAR. In this study, we propose two analytically derived covariances of (i) plane parameters and (ii) plane measurement, for precise extrinsic calibration of camera and LiDAR. These covariances allow the graded approach in planar feature correspondences by exploiting the uncertainty of a set of given features in calibration. To construct plane parameter covariance, we employ the error model of 3D corner points and the analytically formulated plane parameter errors. Next, plane measurement covariance is directly derived from planar regions of point clouds using the out-of-plane errors. In simulation validation, our method is compared to an existing uncertainty-excluding method using the different number of target poses and the different levels of noise. In field experiment, we validated the applicability of the proposed analytic plane covariances for precise calibration using the basic planarity-based method and the latest planarity-and-linearity-based method.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6042-6048
Number of pages7
ISBN (Electronic)9781728173955
DOIs
Publication statusPublished - 2020 May
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: 2020 May 312020 Aug 31

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
CountryFrance
CityParis
Period20/5/3120/8/31

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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