A branch-and-bound algorithm for checkerboard extraction in camera-laser calibration

Abstract

Abstract-We address the problem of camera-to-laserscanner calibration using a checkerboard and multiple imagelaser scan pairs. Distinguishing which laser points measure the checkerboard and which lie on the background is essential to any such system. We formulate the checkerboard extraction as a combinatorial optimization problem with a clear cut objective function. We propose a branch-and-bound technique that deterministically and globally optimizes the objective. Unlike what is available in the literature, the proposed method is not heuristic and does not require assumptions such as constraints on the background or relying on discontinuity of the range measurements to partition the data into line segments. The proposed approach is generic and can be applied to both 3D or 2D laser scanners as well as the cases where multiple checkerboards are present. We demonstrate the effectiveness of the proposed approach by providing numerical simulations as well as experimental results.

Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap

“…Some examples of the calibration targets include a planar checkerboard pattern [ 6 , 7 ] or a right-angled triangular checkerboard [ 17 ] or a trirectangular trihedron [ 8 ] or an arbitrary trihedron [ 18 ] or a v-shaped calibration target [ 19 ] or others. In this category, there are two types of research: sufficient overlap does exist between the field of views of the camera and lidar sensors [ 2 , 6 , 7 , 8 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]; and no overlap [ 29 ]. …”

“…sufficient overlap does exist between the field of views of the camera and lidar sensors [ 2 , 6 , 7 , 8 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]; and…”

“…Khosravian et al [ 28 ] proposed a Branch-and-Bound (BnB) approach for checkerboard extraction in the camera-lidar calibration problem. They formulated the checkerboard extraction as a combinatorial optimization problem with a defined objective function that relies on the underlying geometry of the camera-lidar setup.…”

A joint calibration system of lidar and binocular cameras based on an improved calibration board and DON algorithm

“…static calibration depends on the calibration plate, Zhang, Q et al [1] used the chessboard calibration board to calibrate the single line lidar and vision sensors. Dhall, A et al [2] and Zhou, L et al [3] fitted the corner points by detecting the edge points of the calibration plate under the lidar, to realize the point cloud registration. Khosravian, A et al [4] transformed the chessboard extraction formula into a combinatorial optimization problem with a clear objective function and proposed a branch and bound technique for optimization objectives.…”

“…In the above methods, Dhall, A et al [2] and Zhou, L et al [3] calibrate the traditional calibration board, which has the problem of many and complex point cloud information, so it is necessary to select the point cloud data manually. Jia, Z et al [8] improved the calibration board, but the improvement is too complex and the specific implementation efficiency is low.…”