Obstacle Detection and Tracking Framework for Autonomous Mobile Robots

Abstract Mobile robots face significant challenges in obstacle detection and safe navigation in populated indoor environments. While commonly used, traditional sensors such as a depth camera and a 2D LiDAR exhibit limitations in handling dynamic objects and complex scenarios. Additionally, most current perception pipelines rely on discrete sensors and rarely integrate motion prediction or object tracking, leaving planners blind to rapidly changing obstacles. To address these issues, this study introduces an Obstacle Detection and Tracking Framework that integrates state-of-the-art object detection algorithms—including YOLOv11, MobileNet-SSD and EfficientDet-D0—with multi-object tracking using both DeepSORT and ByteTrack. This tracking information is proactively incorporated into the robot’s occupancy grid through the occupancy grid generation module, where regions predicted to be occupied by moving obstacles are dynamically assigned higher costs. Detected and tracked objects are projected onto a 2D bird’s eye view using depth data, providing spatial and temporal awareness of the environment. To enhance path planning, we add a sensor-fused trajectory predictor. For each tracked object, RGB-D and 2D LiDAR are fused via a Kalman filter to estimate position, velocity of objects for path prediction. This framework bridges the gap between perception and planning systems and provides actionable insights for selecting suitable algorithmic configurations in real-world robot deployment scenarios.