LONS-Calib: Extrinsic Calibration of LiDAR and Omnidirectional Camera With Negative Space

This paper presents LONS-Calib, a novel extrinsic calibration framework for light detection and ranging (LiDAR) and omnidirectional cameras, specifically targeting panoramic annular lenses and fisheye lenses capable of capturing negative space information (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> < 0). It also supports cameras with narrower fields of view, such as pinhole cameras. To overcome the impact of the significant distortion inherent in omnidirectional images, the framework optimizes chess-board corner extraction, projecting these corners onto a unit sphere for accurate camera pose estimation. Additionally, automatic detection of chessboard point clouds enhances efficiency in LiDAR pose estimation. The sample selection process is automated using the Variability of Quality (VOQ) score, which accounts for sensor measurement errors and the generalizability of calibration results. These features ensure that users, regardless of their calibration expertise, can easily record diverse poses and achieve high-quality calibration results with LONS-Calib. Extensive evaluations on synthetic and real-world datasets demonstrate remarkable calibration accuracy and efficiency, with typical optimization times under 0.3s on consumer-grade laptops. The calibration results also demonstrate applicability to multi-sensor fusion simultaneous localization and mapping (SLAM). Our framework is available at: https://github.com/wwendy233/LONS-Calib.