LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

Abstract

LiDAR-camera calibration is a precondition for many heterogeneous systems that fuse data from LiDAR and camera. However, the constraint from common field of view and the requirement for strict time synchronization make the calibration a challenging problem. In this paper, we propose a novel LiDAR-camera calibration method aiming to eliminate these two constraints. Specifically, we capture a scan of 3D LiDAR when both the environment and the sensors are stationary, then move the camera to reconstruct the 3D environment using the sequentially obtained images. Finally, we align 3D visual points to the laser scan based on tightly couple graph optimization method to calculate the extrinsic parameters between LiDAR and camera. Under this design, the configuration of these two sensors are free from the common field of view constraint owing to the extended view from the moving camera. And we also eliminate the requirement for strict time synchronization as we only use the single scan of laser data when the sensors are stationary. We theoretically derive the conditions of minimal observability for our method and prove that the accuracy of calibration is improved by collecting more observations from multiple scattered calibration targets. We validate our method on both simulation platform and real-world datasets. Experiments show that our method achieves higher accuracy than other comparable methods, which is in accordance with our theoretical analysis. In addition, the proposed method is beneficial to not only plane measurement error based chessboard, but also other point measurement error based calibration targets, such as boxes and polygonal boards.

Optical Characterization of the Beams Generated by 3-D LiDARs: Proposed Procedure and Preliminary Results on MRS1000

“…Fig. 15 shows the horizontal dimensions w both with and without using HDDM+ estimated according to (7). In particular, w without HDDM+ has been estimated using n OLS = 13 -n OLS ≈ (t point−cloud t f ire ) + 1 -, whereas w with HDDM+ has been estimated using n OLS = 37 -n OLS ≈ (3⋅t point−cloud t f ire )+1).…”

“…Finally, Fig. 16 shows vertical dimensions h as a function of the distance d calculated as described in (7). Note that the manufacturer makes no distinction between the vertical and horizontal dimensions of the spot (see (8)), thus it is not possible to compare the obtained results with the nominal ones.…”

Camera Calibration Using Multiple Unordered Coplanar Chessboards

“…Thus motivated, this paper is a preliminary study as to whether, and under which conditions, an extended approach based on a set of unordered coplanar chessboards might, in principle, simulate an (infeasibly large) single chessboard, to effectively accommodate longer focusing distances or wider fields of view. Multiple (non-coplanar) or 3D chessboards have already been exploited for camera-to-camera, camera-to-range sensor or multiple depth camera automatic calibration (Geiger et al, 2012;Fuersattel et al, 2017;Yin et al, 2018;Fu et al, 2019;Liu et al 2019). Yet, to the best of our knowledge, the particular use of multiple coplanar chessboards for camera calibration has not been investigated.…”

Estimation of Vehicle Trailer Rotation Using Calibrated Radars With Multiple Viewpoints

“…Multiple sensors are increasingly being deployed in systems, and the sensors are often There are some existing extrinsic calibration methods available for different types of sensors. For instance, extrinsic calibration algorithms are presented for cameras in [38,39], for a combination of lidar and camera in [40][41][42], and for a combination of laser scanner and camera in [43][44][45][46]. We consider automotive radar sensors in this work.…”

“…Some existing calibration methods have been proposed for different sensors; for cameras in [38,39], for lidar and camera in [40][41][42], for laser scanner and camera in [43][44][45][46], for radar and camera in [63][64][65], and for radar, lidar, and camera in [66,67].…”