Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Abstract

Fusion of heterogeneous extroceptive sensors is the most effient and effective way to representing the environment precisely, as it overcomes various defects of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems should be available before precisely fusing the multisensor information. Researchers have proposed several approaches to estimating the extrinsic parameters. These approaches require either auxiliary objects, like chessboards, or extra help from human to select correspondences. In this paper, we proposed a novel extrinsic calibration approach for the extrinsic calibration of range and image sensors. As far as we know, it is the first automatic approach with no requirement of auxiliary objects or any human interventions.First, we estimate the initial extrinsic parameters from the individual motion of the range finder and the camera. Then we extract lines in the image and point-cloud pairs, to refine the line feature associations by the initial extrinsic parameters.At the end, we discussed the degenerate case which may lead to the algorithm failure and validate our approach by simulation. The results indicate high-precision extrinsic calibration results against the ground-truth.

Automatic targetless LiDAR–camera calibration: a survey

“…The appearance information in the surroundings, such as geometric edge alignment, can be useful to reduce such errors. Liao and Liu (2019) utilizes the line features in both the image and the point cloud to refine the calibration parameter by feature matching. A typical pipeline of a hand-eye based method is shown in Fig.…”

“…The camera motion transformations can also be found using a standard visual odometry approach, which estimate the motion of a camera in real time using sequential images (i.e., ego-motion). As an example, ORB-SLAM (Mur-Artal et al, 2015) is a feature-based monocular simultaneous localization and mapping (SLAM) system that is frequently mentioned (Liao andLiu, 2019, Shi et al, 2019a). Note that, the motion estimation purely based on visual estimation faces the problem of scale ambiguity and requires the use of some additional methods to estimate the scale Ishikawa et al (2018), Taylor and Nieto (2016).…”

Targetless Camera-LiDAR Calibration in Unstructured Environments

“…In [35], the authors use road structuring in urban areas, which would limit calibration to these kinds of environments. Instead, in [36], [37], the authors use the co-registration of modeled straight lines in both spaces. In these cases, the problem is that the environment must be highly structured to contain enough geometric primitives.…”

A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

“…The difference in rotation is measured according to the angle difference between the ground truth R gt and the resulting rotation R res , which is calculated as e r = log(R gt R −1 res ) ∨ 4 . The difference in translation is computed using vector subtraction as e t = t gt − t res 2 .…”

“…http://pointclouds.org 2 http://eigen.tuxfamily.org 3 http://ceres-solver.org4 The operator φ = log(R) ∨ is defined to associate R in SO(3) to its rotation angle ϕ ∈ R 3 on the axis.…”