Topic: Robust Localization and Mapping for Robots in Dynamic Environments

As the demand for autonomous robots in real-world environments grows, the realization of simultaneous localization and mapping (SLAM) for robots in dynamic environments has become a challenging and much-anticipated research area. Traditional SLAM methods mainly target static environments, while the variability of real-world environments, such as moving objects and dynamic obstacles, greatly affects the accuracy and stability of SLAM systems. Therefore, investigating how to achieve robust localization and mapping techniques in non-static environments is crucial for the wide application of autonomous robots.

This Special Issue aims to bring together and explore the latest research advances and technological breakthroughs in realizing robust SLAM in dynamic environments. It will focus on innovative SLAM algorithms, sensor fusion techniques, light detection and ranging (LiDAR) and visual SLAM methods, and strategies to improve the robustness of SLAM systems in dynamic environments. By highlighting these cutting-edge technologies, the Special Issue seeks to promote the application and development of SLAM technology in dynamic settings and further enhance the adaptability and operational efficiency of autonomous robots in real-world scenarios. Potential topics of this Special Issue include but are not limited to the following:
● Sensor Fusion in Dynamic Environments;
● Application of Vision and LiDAR in SLAM;
● Real-time Optimization of SLAM Performance;
● Detection and Avoidance of Dynamic Obstacles;
● Localization and Tracking for Robots in Non-Static Environments.

SLAM, sensor fusion, LiDAR, motion tracking, obstacle detection, path planning

30 Apr 2025