Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild

TL;DR

A new targetless cross-modal calibration framework by focusing on the extrinsic transformations among stereo cameras, thermal cameras, and laser sensors is demonstrated, which requires no dedicated targets and performs the multi-sensor calibration in a single shot without human interaction.

Abstract

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.

Figures (12)

• Figure 1: Thermal mapping on a dense reconstructed point cloud with the thermal extrinsic calibrated by our method.
• Figure 2: Flow chart of our calibration framework. It takes synchronized stereo image pairs, thermal images, and laser point clouds as input, and automatically calibrates the laser-stereo and thermal-stereo transformations in one system.
• Figure 3: Example of the matched feature points in a stereo image pair (left) and the generated stereo point cloud (right).
• Figure 4: Example of the matched features on edges in a stereo image pair (left) and the edge points (marked in red) detected in the stereo point cloud (right).
• Figure 5: Left: Schematic diagram of laser edge points detection with sampling radius $k=3$. Right: The detected edge points (marked in red) in a laser point cloud.