Obstacle Detection From Roadside Laser Scans

The detection of objects on a given road path by vehicles equipped with range measurement devices is important to many civilian and military applications such as obstacle avoidance in autonomous navigation systems. In this thesis, we develop a method to detect objects of a specific size lying on a road using an acquisition vehicle equipped with forward looking Light Detection And Range (LiDAR) sensors and inertial navigation system. We use GPS data to accurately place the LiDAR points in a world map, extract point cloud clusters protruding from the road, and detect objects of interest using weighted random forest trees. We show that our proposed method is effective in identifying objects for several road datasets collected with various object locations and vehicle speeds.