Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties

TL;DR

This paper introduces a real-time multi-object detection and tracking framework for urban autonomous vehicles using 3D LIDAR, effectively handling uncertainties and sensor limitations through occlusion-aware detection and probabilistic tracking.

Contribution

It presents a novel integrated approach combining occlusion-aware detection with heuristics and adaptive probabilistic tracking tailored for urban scenarios with 3D LIDAR data.

Findings

Achieves promising tracking performance in urban environments

Outperforms some state-of-the-art methods in real-world tests

Handles uncertainties from sensing limitations and target movement

Abstract

Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation.