Autonomous Navigation in Crowded Space Using Multi-Sensory Data Fusion
Nourin Siddique Ananna, Mollah Md Saif, Maisha Noor, Ishrat Tasnim Awishi, Md. Khalilur Rhaman, Md. Golam Rabiul Alam
Abstract
Autonomous navigation in crowded environments remains a significant challenge due to the highly dynamic and unpredictable nature of pedestrian movements. This paper presents a novel approach for socially-compliant crowd navigation by leveraging human pose tracking, trajectory prediction, and obstacle avoidance techniques. We introduce PoseTrajNet, an end-to-end autonomous agent navigation pipeline that integrates YOLOv8 for object detection, BlazePose for real-time human pose estimation, and a custom trajectory prediction model drawing on concepts from Social GANs. PoseTrajNet employs pose keypoints as socially-compliant features to anticipate pedestrian trajectories, enabling proactive path planning and dynamic safe radius adjustments for obstacle avoidance. Extensive evaluations on standard datasets demonstrate PoseTrajNet's effectiveness in seamless crowd navigation, outperforming baselines while adhering to social norms.
BibTeX
@inproceedings{icra2025_autonomousnaviga,
title = {Autonomous Navigation in Crowded Space Using Multi-Sensory Data Fusion},
author = {Nourin Siddique Ananna and Mollah Md Saif and Maisha Noor and Ishrat Tasnim Awishi and Md. Khalilur Rhaman and Md. Golam Rabiul Alam},
booktitle = {ICRA 2025},
year = {2025}
}