  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.
