Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild
This addresses the need for efficient calibration in autonomous driving and robotics by eliminating targets and human labor, though it is incremental as it builds on existing calibration methods.
The paper tackles the problem of multi-sensor extrinsic calibration without targets by proposing a framework for stereo cameras, thermal cameras, and laser sensors, achieving accurate results in general scenes.
The fusion of multi-modal sensors has become increasingly popular in autonomous driving and intelligent robots since it can provide richer information than any single sensor, enhance reliability in complex environments. Multi-sensor extrinsic calibration is one of the key factors of sensor fusion. However, such calibration is difficult due to the variety of sensor modalities and the requirement of calibration targets and human labor. In this paper, we demonstrate a new targetless cross-modal calibration framework by focusing on the extrinsic transformations among stereo cameras, thermal cameras, and laser sensors. Specifically, the calibration between stereo and laser is conducted in 3D space by minimizing the registration error, while the thermal extrinsic to the other two sensors is estimated by optimizing the alignment of the edge features. Our method requires no dedicated targets and performs the multi-sensor calibration in a single shot without human interaction. Experimental results show that the calibration framework is accurate and applicable in general scenes.