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Monocular Vision-based Obstacle Avoidance Scheme for Micro Aerial Vehicle Navigation

Published: 20 May 2021

Samuel Karlsson, Christoforos Kanellakis, Sina Sharif Mansouri and George Nikolakopoulos

International Conference on Unmanned Aircraft Systems (ICUAS), 2021

One of the challenges in deploying Micro Aerial Vehicles (MAVs) in unknown environments is the need of securing for collision-free paths with static and dynamic obstacles. This article proposes a monocular vision-based reactive planner for MAVs obstacle avoidance. The avoidance scheme is structured around a Convolution Neural Network (CNN) for object detection and classification (YOLO), used to identify the bounding box of the objects of interest in the image plane. Moreover, the CNN is combined with a Kalman filter to robustify the object tracking, in case of loosing the boundary boxes, by estimating their position and providing a fixed rate estimation. Since MAVs are fast and agile platforms, the object tracking should be performed in real-time for the collision avoidance. By processing the information of the bounding boxes with the image field of view and applying trigonometry operations, the pixel coordinates of the object are translated to heading commands, which results to a collision free maneuver. The efficacy of the proposed scheme has been extensively evaluated in the Gazebo simulation environment, as well as in experimental evaluations with a MAV equipped with a monocular camera.