Developing detailed a priori 3D models of large environments to aid in robotic navigation tasks

In order to effectively navigate any environment, a robotic vehicle needs to understand the terrain and obstacles native to that environment. Knowledge of its own location and orientation, and knowledge of the region of operation, can greatly improve the robot’s performance. To this end, we have developed a mobile system for the fast digitization of large-scale environments to develop the a priori information needed for prediction and optimization of the robot’s performance. The system collects ground-level video and laser range information, fusing them together to develop accurate 3D models of the target environment. In addition, the system carries a differential Global Positioning System (GPS) as well as an Inertial Navigation System (INS) for determining the position and orientation of the various scanners as they acquire data. Issues involved in the fusion of these various data modalities include: Integration of the position and orientation (pose) sensors’ data at varying sampling rates and availability; Selection of “best” geometry in overlapping data cases; Efficient representation of large 3D datasets for real-time processing techniques. Once the models have been created, this data can be used to provide a priori information about negative obstacles, obstructed fields of view, navigation constraints, and focused feature detection.