High-Precision Extrinsic Calibration Method of a Time-of-Flight IMU RGB-Camera With Loop Closure Constraints

Accurate localization is key to realizing an autonomous mobile robot. The fusion of depth data measured by a time-of-flight (TOF) camera with visual-inertial simultaneous localization and mapping (SLAM) can greatly improve depth estimation and mapping. To achieve a robust sensor fusion system, precise calibration of one sensor to another is essential. In this paper, we propose a joint extrinsic calibration method for a TOF- IMU RGB-camera system. Further, we design a pattern that consists of white circles with high reflectivity, and a black background with high absorptivity. Using our calibration method, circular features can be accurately extracted from amplitude images as well as from RGB images. We correct the TOF depth measurement errors, which depend on the pixel location and distance of the pixel away from the object, by establishing an error distribution model with a B-spline function. All sensors are fixed on a platform, and the position of each sensor relative to the other sensors is unchanged, forming loop closure constraints. Based on this prerequisite, we utilize the loop closure constraints between multiple sensors to obtain more accurate extrinsic parameters. By taking the loop closure constraints into account, the stability and robustness of the calibration is increased. The experimental results validate the proposed method and prove its accuracy. Thus, we achieve high precision in multi-sensor extrinsic calibration, which is the necessary foundation of SLAM research.

Automated summary

Accurate localization is crucial for autonomous mobile robots. By combining depth data from a TOF camera with visual-inertial SLAM, depth estimation and mapping can be significantly improved. Precise calibration between sensors is essential for a robust sensor fusion system.