Research on the method of multi-sensor data fusion for mobile robots

doi:10.16731/j.cnki.1671-3133.2024.07.009

Abstract

Abstract: Multi-sensor data fusion technology can better solve the problem of data incompatibility generated by smart devices,improve device operational efficiency and productivity. Existing methods lack high-performance performance and do not consider data privacy protection issues. It innovatively introduced federated learning into multi-sensor data fusion. The federated learning local model used the Gated Recurrent Unit (GRU) algorithm to solve the multi-sensor data fitting problem. A novel parallel stereoscopic multi-sensor data fusion method was designed for the first time,which has excellent fusion performance and ensures the privacy of each client′s data. The experimental results demonstrate the correctness and rationality of this method,as well as its advantages in robustness.

Key words: mobile robots, multi-sensor, data fusion, federated learning

CLC Number:

TP391
TH741

FENG Jing, WEI Hangxin, YUE Gaofeng, WANG Yukun, QIN Le, XI Wenkui, SUN Wen. Research on the method of multi-sensor data fusion for mobile robots[J]. Modern Manufacturing Engineering, 2024, 526(7): 69-76.

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