This study presents an innovative multi-sensor fusion SLAM solution to address the significant error in particle proposal distribution, excessive particle resource consumption, and low algorithm execution efficiency encountered in traditional RBPF-SLAM algorithms in practical applications. The proposed solution aims to optimize the overall performance of the SLAM system by effectively integrating data from laser radar, inertial measurement unit (IMU), and wheel odometer, among other multi-source sensors.
Hongxi ChenZhengguang MaZhaoyang LiPeng WangYongfei Xiao
Zhixiang LiuChunxue XieMiao XieMao Jun
Jionglin HeJiaxiang FangShuping XuDingzhe Yang
Yang TaoYuanzi HeXuemei MaHaidong XuJingbo HaoJunrong Feng
Jun DaiYingying LeiJunwei ZhaoYundong Mei