LiDAR-camera system extrinsic calibration by establishing virtual point correspondences from pseudo calibration objects

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Abstract

Extrinsic calibration on LiDAR-camera system without specific calibration objects is a challenging task, for it is difficult to find point correspondences from RGB image and sparse LiDAR point cloud. In a natural scene, some objects if satisfying three conditions can be regarded as pseudo calibration objects. In this paper, we propose the virtual point correspondence at the first time. It is established from the 2D box of one pseudo calibration object in RGB image and its corresponding 3D frustum box in point cloud. Based on virtual point correspondence, we present a novel LiDAR-camera extrinsic calibration method without specific calibration objects. It requires two calibration conditions that easily satisfied in the practical application. A normal guided foreground detection method is proposed to automatically extract 3D frustum box. After that, a geometrical optimization scheme is presented to estimate the extrinsic parameters with the virtual point correspondences. Simulations and real data experiments demonstrate that our method is accurate, robust, and outperforms state-of-the-art calibration object based method.

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