Continuous Target-free Extrinsic Calibration of a Multi-Sensor System from a Sequence of Static Viewpoints
This addresses the need for precise sensor calibration in robotics to improve tasks like SLAM, though it appears incremental as it builds on existing point cloud matching techniques without a new paradigm.
The paper tackles the problem of extrinsic calibration for multi-sensor systems in mobile robotics by proposing a continuous, target-free method based on matching point clouds from static viewpoints, demonstrating its suitability on a system with 2 lidars, 3 cameras, and a radar.
Mobile robotic applications need precise information about the geometric position of the individual sensors on the platform. This information is given by the extrinsic calibration parameters which define how the sensor is rotated and translated with respect to a fixed reference coordinate system. Erroneous calibration parameters have a negative impact on typical robotic estimation tasks, e.g. SLAM. In this work we propose a new method for a continuous estimation of the calibration parameters during operation of the robot. The parameter estimation is based on the matching of point clouds which are acquired by the sensors from multiple static viewpoints. Consequently, our method does not need any special calibration targets and is applicable to any sensor whose measurements can be converted to point clouds. We demonstrate the suitability of our method by calibrating a multi-sensor system composed by 2 lidar sensors, 3 cameras, and an imaging radar sensor.