Researches advanced in the SLAM for dynamic environment

• Published in: Journal of physics. Conference series Vol. 2634; no. 1; pp. 12054 - 12060
• Main Author: Li, Ying
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.11.2023
• Subjects: Algorithms; Computer vision; deep learning; dynamic environment; Navigation; Physics; Simultaneous localization and mapping; SLAM; Three dimensional models
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/2634/1/012054

Simultaneous Localization and Mapping (SLAM) has always been a popular topic in the computer vision community, as it seeks to forecast the location of agents and use sensors to detect their surroundings in order to construct maps and perform navigation. The majority of SLAM algorithms assume that objects in the environment are immobile or in slow motion, whereas the SLAM system in a dynamic world remains an unanswered question. When constructing a 3D point cloud map by sequentially accumulating scanning data, dynamic objects typically leave undesirable traces in the map. The traces of these dynamic items operate as impediments, hindering the positioning and navigation performance of mobile vehicles. In this work, we discuss the most recent advancements in SLAM in a dynamic context. Specifically, this paper examines three characteristics of the most significant negative effects that the dynamic environment has on SLAM. Then, according to the dynamic degree, solutions are presented for various dynamic items, including the design concept, basic framework, advantages, and disadvantages. We conclude by discussing the existing research difficulties in the subject of dynamic SLAM and its projected development path.