Obstacle detection for autonomous navigation : an LDRD final report.

This report summarizes the analytical and experimental efforts for the Laboratory Directed Research and Development (LDRD) project entitled 'Obstacle Detection for Autonomous Navigation'. The principal goal of this project was to develop a mathematical framework for obstacle detection. The framework provides a basis for solutions to many complex obstacle detection problems critical to successful autonomous navigation. Another goal of this project was to characterize sensing requirements in terms of physical characteristics of obstacles, vehicles, and terrain. For example, a specific vehicle traveling at a specific velocity over a specific terrain requires a sensor with a certain range of detection, resolution, field-of-view, and sufficient sensitivity to specific obstacle characteristics. In some cases, combinations of sensors were required to distinguish between different hazardous obstacles and benign terrain. In our framework, the problem was posed as a multidimensional, multiple-hypothesis, pattern recognition problem. Features were extracted from selected sensors that allow hazardous obstacles to be distinguished from benign terrain and other types of obstacles. Another unique thrust of this project was to characterize different terrain classes with respect to both positive (e.g., rocks, trees, fences) and negative (e.g., holes, ditches, drop-offs) obstacles. The density of various hazards per square kilometer was statistically quantified for different terrain categories (e.g., high desert, ponderosa forest, and prairie). This quantification reflects the scale, or size, and mobility of different types of vehicles. The tradeoffs between obstacle detection, position location, path planning, and vehicle mobility capabilities were also to be characterized