Simultaneous Localization and Mapping Algorithm Based on the Asynchronous Fusion of Laser and Vision Sensors

Kexin Xing; Xingsheng Zhang; Yegui Lin; Wenqi Ci; Wei Dong

doi:10.3389/fnbot.2022.866294

Simultaneous Localization and Mapping Algorithm Based on the Asynchronous Fusion of Laser and Vision Sensors

Front Neurorobot. 2022 May 24:16:866294. doi: 10.3389/fnbot.2022.866294. eCollection 2022.

Authors

Kexin Xing¹, Xingsheng Zhang¹, Yegui Lin¹, Wenqi Ci¹, Wei Dong²

Affiliations

¹ College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.
² State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.

Abstract

In this paper, a simultaneous localization and mapping algorithm based on the weighted asynchronous fusion of laser and vision sensors is proposed for an assistant robot. When compared to the synchronous fusion algorithm, this method can effectively use the redundant data in the vision sensor and improve the tracking accuracy of the algorithm. At the same time, the attitude estimation of the visual sensor is taken as a prior of the attitude estimation of the laser sensor to reduce the number of iterations and improve the efficiency of the algorithm. Further, according to the running state of the robot, a weighting coefficient based on angle is introduced to improve the confidence of the measurement. Experimental results show that the algorithm is robust and can work in a degraded environment. When compared to the synchronous fusion method, the asynchronous fusion algorithm has a more accurate prior, faster operation speed, higher pose estimation frequency, and more accurate positioning accuracy.

Keywords: SLAM; data fusion; laser; multi-rate; vision.