Targetless Extrinsic Calibration of Stereo, Thermal, and Laser Sensors in Structured Environments

A 2-D and 3-D sensor extrinsic calibration is the key prerequisite for multisensor-based robot perception and localization. However, such calibration is challenging due to the variety of sensor modalities and the requirement of special calibration targets and human intervention. In this article, we demonstrate a new targetless cross-modal calibration system focusing on the extrinsic transformations among stereo cameras, thermal cameras, and laser sensors. Specifically, the calibration between stereo and laser is conducted in 3-D 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. Due to the low contrast of thermal images, the extracted edges are often noisy, resulting in incorrect edge matching. We introduce the edge alignment optimization on attraction field map to overcome this challenge. Our method requires no dedicated targets and performs the multisensor calibration in a single shot without human interaction. Extensive experiments on our collected real-world datasets show that our system can be easily used in structured environments with high extrinsic calibration accuracy. The video demonstration can be found at https://www.youtube.com/watch?v=b1QAVY7hUu8. The code is released to the public at https://github.com/FuTaimeng/auto_calibration.

Automated summary

In this article, a targetless cross-modal calibration system is presented for extrinsic calibration among stereo cameras, thermal cameras, and laser sensors. The calibration is done by minimizing registration error and optimizing edge feature alignment. The method does not rely on dedicated targets and allows for multisensor calibration in a single shot.