Dynamic Multi-Object Analysis Using Particles for Social Navigation

Autonomous robot navigation in social environments, such as crowded hallways, is a challenging task. Robots need to monitor the movement directions of surrounding objects, search for collision-free routes , and update their paths in real -time to navigate safely. To address this challenge, we present dynamic multi-object analysis framework for robust and efficient social navigation. To ensure consistent tracking performance, we adopt an optimized feature matching algorithm, combined with particle filter to classify static and dynamic points effectively. We evaluate our proposed approach through simulation testing. Additionally, we have released the project’s source code and supplementary materials, including a video demonstrating experimental results on GitHub (https://github.com/iminolee/SCAN).