1 research outputs found
Localization of Unmanned Aerial Vehicles in Corridor Environments using Deep Learning
Vision-based pose estimation of Unmanned Aerial Vehicles (UAV) in unknown
environments is a rapidly growing research area in the field of robot vision.
The task becomes more complex when the only available sensor is a static single
camera (monocular vision). In this regard, we propose a monocular vision
assisted localization algorithm, that will help a UAV to navigate safely in
indoor corridor environments. Always, the aim is to navigate the UAV through a
corridor in the forward direction by keeping it at the center with no
orientation either to the left or right side. The algorithm makes use of the
RGB image, captured from the UAV front camera, and passes it through a trained
deep neural network (DNN) to predict the position of the UAV as either on the
left or center or right side of the corridor. Depending upon the divergence of
the UAV with respect to the central bisector line (CBL) of the corridor, a
suitable command is generated to bring the UAV to the center. When the UAV is
at the center of the corridor, a new image is passed through another trained
DNN to predict the orientation of the UAV with respect to the CBL of the
corridor. If the UAV is either left or right tilted, an appropriate command is
generated to rectify the orientation. We also propose a new corridor dataset,
named NITRCorrV1, which contains images as captured by the UAV front camera
when the UAV is at all possible locations of a variety of corridors. An
exhaustive set of experiments in different corridors reveal the efficacy of the
proposed algorithm.Comment: 9 pages, 7 figure