IIIT Hyderabad Publications |
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Towards Autonomous Navigation and Geo-tagging with Monocular MAVsAuthor: Shah Utsav Dipakkumar Date: 2017-12-06 Report no: IIIT/TH/2017/86 Advisor:Madhava Krishna AbstractRecently, the interest in Micro Aerial Vehicles (MAVs) and their autonomous flights has increased tremendously and significant advances have been made. The monocular camera has turned out to be most popular sensing modality for MAVs as it is light-weight, does not consume more power, and encodes rich information about the environment around. In this work, we present DeepFly, our framework for autonomous navigation of a quadcopter equipped with monocular camera. The navigable space detection and waypoint selection are fundamental components of autonomous navigation system. They have broader meaning than just detecting and avoiding immediate obstacles. Finding the navigable space emphasizes equally on avoiding obstacles and detecting ideal regions to move next to. The ideal region can be defined by two properties: 1) All the points in the region have approximately same high depth value and 2) The area covered by the points of the region in the disparity map is considerably large. The waypoints selected from these navigable spaces assure collision-free path which is safer than path obtained from other waypoint selection methods which do not consider neighboring information. In our approach, we obtain a dense disparity map by performing a translation maneuver. This disparity map is input to a deep neural network which predicts bounding boxes for multiple navigable regions. Our deep convolutional neural network with shortcut connections regresses variable number of outputs without any complex architectural add on. Our autonomous navigation approach has been successfully tested in both indoors and outdoors environment and in range of lighting conditions. We also propose a novel pipeline for detecting, localizing, and recognizing trees with a quadcoptor equipped with monocular camera. The quadcoptor flies in an area of semi-dense plantation filled with many trees of more than 5 meter in height. Trees are detected on a per frame basis using state of the art Convolutional Neural Networks inspired by recent rapid advancements showcased in Deep Learning literature. Once detected, the trees are tagged with a GPS coordinate through our global localizing and positioning framework. Further the localized trees are segmented, characterized by feature descriptors, and stored in a database by their GPS coordinates. In a subsequent run in the same area, the trees that get detected are queried to the database and get associated with the trees in the database. The association problem is posed as a dynamic programming problem and the optimal association is inferred. The algorithm has been verified in various zones in our campus infested with trees with varying density on the Bebop 2 drone equipped with fisheye wide angle vision. High percentage of successful recognition and association of the trees between two or more runs is the cornerstone of this effort. The proposed method is also able to identify if trees are missing from their expected GPS tagged locations thereby making it possible to immediately alert concerned authorities about possible unlawful felling of trees. We also propose a novel way of obtaining dense disparity map for quadcopter with monocular camera. Full thesis: pdf Centre for Robotics |
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