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Computer Vision Driven Micro-Aerial Vehicle (MAV): Obstacles Avoidance. Lim-Kwan (Kenny) Kong - Graduate Student Dr. Jie Sheng - Faculty Advisor Dr. Ankur Teredesai - Faculty Advisor. The Idea. Using quad-rotor (quad-copter) Stream video using mono camera
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Computer Vision Driven Micro-Aerial Vehicle (MAV): Obstacles Avoidance • Lim-Kwan (Kenny) Kong - Graduate Student • Dr. Jie Sheng - Faculty Advisor • Dr. Ankur Teredesai - Faculty Advisor
The Idea • Using quad-rotor (quad-copter) • Stream video using mono camera • Detect static obstacles using computer vision algorithms • Avoid the obstacles
Steps • Reverse engineer the AR.Drone • Implement 2-3 obstacle avoidance algorithms • Testing • Collaboration: • Ji’s object tracking algorithm • Sid’s Pi-map reduce algorithm
Obstacle avoidance Algorithm Figure 2. Optical flow differences [2] Figure 1. The basic program flow
Outcome • Basic • Avoids obstacles in flight • Advanced • Avoids obstacles in flight • while tracking a designated object (Ji) • using Pi-map reduce algorithm (Sid).
References • [1] A. Eresen, N. Imamoglu and M. O. Efe. Autonomous quadrotor flight with vision-based obstacle avoidance in virtual environment. Expert Syst. Appl. 39(1), pp. 894-905. 2012. Available: http://dx.doi.org/10.1016/j.eswa.2011.07.087. DOI: 10.1016/j.eswa.2011.07.087. • [2] W. Benn and S. Lauria. Robot navigation control based on monocular images: An image processing algorithm for obstacle avoidance decisions. Mathematical Problems in Engineering pp. 240476 (14 pp.). 2012. Available: http://dx.doi.org/10.1155/2012/240476. DOI: 10.1155/2012/240476. • [3] D. J. LeBlanc and N. H. McClamroch. Adaptive processing for vision-based ranging. Presented at American Control Conference, 1993. • [4] Huili Yu, R. W. Beard and J. Byrne. Vision-based local multi-resolution mapping and path planning for miniature air vehicles. Presented at American Control Conference, 2009. ACC '09. 2009, . DOI: 10.1109/ACC.2009.5160065.