A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration
This addresses a specific calibration challenge in robotics and computer vision, offering a non-heuristic solution, though it appears incremental relative to existing methods.
The paper tackles the problem of extracting checkerboard points from laser scans for camera-laser calibration by formulating it as a combinatorial optimization problem and proposes a branch-and-bound algorithm that deterministically optimizes the objective, demonstrating effectiveness through numerical simulations and experiments.
We address the problem of camera-to-laser-scanner calibration using a checkerboard and multiple image-laser 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.