3D-LIDAR Multi Object Tracking for Autonomous Driving

3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties

Arya Senna Abdul Rachman, Arya (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control)

Campoy Cervera, P. (mentor)
Sefati, Mohsen (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2017-11-09

The recent advancement of the autonomous vehicle has raised the need for reliable environmental perception. This is evident, as an autonomous vehicle has to perceive and interpret its local environment in order to execute reactive and predictive control action. Object Tracking is an integral part of vehicle perception, as it enables the vehicle to estimate surrounding objects trajectories to achieve dynamic motion planning. The 3D LIDAR has been widely used in object tracking research since the mechanically compact sensor provides rich, far-reaching and real-time data of spatial information around the vehicle. On the other hand, the development of autonomous driving is heading toward its use in the urban-driving situation. In an urban situation, a robust detection and tracking algorithm is required due to increasing number of Vulnerable Road User (e.g. pedestrian and cyclist), heterogeneous terrain, inherent measurement uncertainties and limited sensor reach.

This thesis presents an integrated framework of multi-target
object detection and tracking using 3D LIDAR geared toward urban use. The framework combines occlusion-aware detection methods, probabilistic adaptive filtering and computationally efficient heuristics logic-based filtering to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The implemented framework takes a raw 3D LIDAR data as input to perform multi-target object detection while simultaneously maintaining track of the detected objects' kinematic states and dimension in robust, causal, and real-time manner.

Robust detection is enabled by slope-based ground removal and L-shape fitting to reliably enclose bounding box to objects of interest in the presence of sensor occlusion. The tracker utilises three combined Bayesian filters (IMM-UK-JPDAF) which simultaneously tackle association uncertainties, motion uncertainties and estimate non-linear stochastic motion model in real-time. Logic-based rule filters are also designed to augment the rest of detection and tracking based on the understanding of LIDAR sensor limitation and occlusion characteristic.

The evaluation results using real-world pre-recorded 3D LIDAR data show the proposed framework can achieve promising real-time tracking performance in urban situations. Diverse datasets are deliberately chosen to evaluate if the MOT system is capable of working in a varying driving scenario. The benchmark results highlight that the designed and implemented MOT system is performing on par with the state-of-art works and yield satisfying accuracy and precision in most given urban settings.

Multi-object Tracking
Vehicle Perception
3D LIDAR
Object detection
Object tracking
Probabilistic Tracking
Autonomous driving
Machine vision
Adaptive Filtering
Heuristic Rules

http://resolver.tudelft.nl/uuid:f536b829-42ae-41d5-968d-13bbaa4ec736

50.7815082,6.0463215

Student theses

master thesis

© 2017 Arya Arya Senna Abdul Rachman