Spatiotemporal Calibration of Camera and 3D Laser Scanner
This addresses the need for robust sensor fusion in applications like robotics or autonomous systems, though it is incremental as it builds on existing calibration methods.
The paper tackles the problem of calibrating camera and 3D laser scanner setups under dynamic conditions by proposing an open-source spatiotemporal calibration framework using chessboard markers, achieving accurate and repeatable results with a one-minute calibration time.
The multi-sensory setups consisting of the laser scanners and cameras are popular as the measurements complement each other and provide necessary robustness for applications. Under dynamic conditions or when in motion, a direct transformation (spatial calibration) and time offset between sensors (temporal calibration) is needed to determine the correspondence between measurements. We propose an open-source spatiotemporal calibration framework for a camera and a 3D laser scanner. Our solution is based on commonly available chessboard markers requiring one-minute calibration before the operation that offers accurate and repeatable results. The framework is based on batch optimization of point-to-plane constraints with a time offset calibration possible by a novel continuous representation of the plane equations based on a minimal representation in the Lie algebra and the use of B-splines. The framework's properties are evaluated in simulation while correctness is verified with two distinct sensory setups with Velodyne VLP-16 and SICK MRS6124 3D laser scanners.