Obstacle Avoidance For Unmanned Air Vehicles Using Image Feature Tracking

This paper discusses a computer vision algorithm and a control law for obstacle avoidance for small unmanned air vehicles using a video camera as the primary sensor. Small UAVs are used for low altitude surveillance flights where unknown obstacles can be encountered. Small UAVs can be given the capability to navigate in uncertain environments if obstacles are identified. This paper presents an obstacle detection methodology using feature tracking in a forward looking, onboard camera. Features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the salient features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially problematic areas using agglomerative clustering. The small UAV employs a sliding mode control law in the autopilot to avoid obstacles.