Heterogeneous Multi-sensor Calibration based on Graph Optimization
This work addresses calibration inconsistencies for robotics and mapping systems that use multiple sensors, but it appears incremental as it builds on existing graph optimization techniques.
The paper tackles the problem of inconsistent extrinsic parameter calibration across multiple sensors in robotics by proposing a graph-based refinement method, demonstrating its effectiveness on a platform with twelve sensors and reporting great performance.
Many robotics and mapping systems contain multiple sensors to perceive the environment. Extrinsic parameter calibration, the identification of the position and rotation transform between the frames of the different sensors, is critical to fuse data from different sensors. When obtaining multiple camera to camera, lidar to camera and lidar to lidar calibration results, inconsistencies are likely. We propose a graph-based method to refine the relative poses of the different sensors. We demonstrate our approach using our mapping robot platform, which features twelve sensors that are to be calibrated. The experimental results confirm that the proposed algorithm yields great performance.