Tracking and data association
Robot Motion Planning
Vision-based local multi-resolution mapping and path planning for miniature air vehicles
ACC'09 Proceedings of the 2009 conference on American Control Conference
Expansion segmentation for visual collision detection and estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Small Unmanned Aircraft: Theory and Practice
Small Unmanned Aircraft: Theory and Practice
Monocular Visual Mapping for Obstacle Avoidance on UAVs
Journal of Intelligent and Robotic Systems
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This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained. Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman filter that takes into account the correlations between obstacles. We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the rapidly-exploring random tree algorithm. The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.