Open AccessResearch on SLAM based on RBPF algorithm in indoor environment
Author(s) -
Qiang Li,
Jia Kang,
Xiaofang Cao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1971/1/012066
Subject(s) - simultaneous localization and mapping , mobile robot , resampling , computer science , computer vision , particle filter , artificial intelligence , lidar , robot , geography , remote sensing , filter (signal processing)
In order to realize the simultaneous localization and mapping (SLAM) of robots in indoor environments, a SLAM method for four-wheel mobile robots based on RBPF algorithm and lidar is proposed. The mobile robot realizes its own positioning during the movement. The lidar scans the location of indoor obstacles, updates the map in real time, and gradually realizes the construction of a local map to a global map through data association. Aiming at the particle barrenness that may occur when the RBPF algorithm realizes SLAM during resampling, an adaptive resampling method is adopted to ensure that there are enough particles to realize SLAM every time. The experimental results show that when the linear velocity and angular velocity of the four-wheel mobile robot are small, indoor SLAM can be better realized.