An Interactive LiDAR to Camera Calibration

TL;DR

This paper presents an interactive toolbox for LiDAR-camera calibration that simplifies the process by automatically detecting planar board corners and using genetic algorithms, improving robustness and ease of use in long-range scenarios.

Contribution

The introduced toolbox automates corner detection and supports multiple camera models, reducing the need for precise calibration targets and enhancing calibration robustness.

Findings

Robust calibration results with Velodyne VLP-16 LiDAR and Point Grey Chameleon 3 camera.

Automatic corner detection simplifies the calibration process.

Supports various camera models including fisheye and pinhole.

Abstract

Recent progress in the automated driving system (ADS) and advanced driver assistant system (ADAS) has shown that the combined use of 3D light detection and ranging (LiDAR) and the camera is essential for an intelligent vehicle to perceive and understand its surroundings. LiDAR-camera fusion requires precise intrinsic and extrinsic calibrations between the sensors. However, due to the limitation of the calibration equipment and susceptibility to noise, algorithms in existing methods tend to fail in finding LiDAR-camera correspondences in long-range. In this paper, we introduced an interactive LiDAR to camera calibration toolbox to estimate the intrinsic and extrinsic transforms. This toolbox automatically detects the corner of a planer board from a sequence of LiDAR frames and provides a convenient user interface for annotating the corresponding pixels on camera frames. Since the toolbox only detects the top corner of the board, there is no need to prepare a precise polygon planar board or a checkerboard with different reflectivity areas as in the existing methods. Furthermore, the toolbox uses genetic algorithms to estimate the transforms and supports multiple camera models such as the pinhole camera model and the fisheye camera model. Experiments using Velodyne VLP-16 LiDAR and Point Grey Chameleon 3 camera show robust results.