LIDAR, Camera, and Inertial Sensor Based Navigation and Positioning Techniques for Advances ITS Applications

Sensor fusion techniques have been used for years to combine sensory data from disparate sources. This dissertation focuses on LIDAR, camera and inertial sensors based navigation and vehicle positioning techniques. First of all, a unique multi-planar LIDAR and computer vision calibration algorithm is proposed. This approach requires the camera and LIDAR to observe a planar pattern. Then the geometric constraints of the ’views’ from the LIDAR and camera images are resolved as the coordinate transformation and rotation coefficients. A tightly coupled LIDAR/CV integrated system for vehicle detection and tracking is also proposed in this dissertation. The LIDAR sensor data is transformed into the image coordinates. Different Regions of Interest (ROIs) in the imagery are defined based on the LIDAR object hypotheses. Then a classifier error correction approach is used to choose an optimal position of the detected vehicle. We also present an autonomous positioning solution for urban environment. The positioning solution is derived by combining measurements from both LIDAR and inertial sensors. The last chapter of this dissertation is vehicle tracking used in traffic surveillance system. A real-time multi-vehicle tracking approach is proposed, which combines both local feature tracking and a global color probability model.