/Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences

Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences

Lipu Zhou, Zimo Li and Michael Kaess
Conference Paper, IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, October, 2018

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Abstract

In this paper, we address the problem of extrinsic calibration of a camera and a 3D Light Detection and Ranging (LiDAR) sensor using a checkerboard. Unlike previous works which require at least three checkerboard poses, our algorithm reduces the minimal number of poses to one by combining 3D line and plane correspondences. Besides, we prove that parallel planar targets with parallel boundaries provide the same constraints in our algorithm. This allows us to place the checkerboard close to the LiDAR so that the laser points better approximate the target boundary without loss of generality. Moreover, we present an algorithm to estimate the similarity transformation between the LiDAR and the camera for the applications where only the correspondences between laser points and pixels are concerned. Using a similarity transformation can simplify the calibration process since the physical size of the checkerboard is not needed. Meanwhile, estimating the scale can yield a more accurate result due to the inevitable measurement errors of the checkerboard size and the LiDAR intrinsic scale factor that transforms the LiDAR measurement to the metric measurement. Our algorithm is validated through simulations and experiments. Compared to the plane-only algorithms, our algorithm can obtain more accurate result by fewer number of poses. This is beneficial to the large-scale commercial application.

BibTeX Reference
@conference{Zhou-2018-107715,
author = {Lipu Zhou and Zimo Li and Michael Kaess},
title = {Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences},
booktitle = {IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS},
year = {2018},
month = {October},
}
2018-09-21T10:17:29+00:00