DROAN – Disparity-space Representation for Obstacle AvoidaNce: Enabling Wire Mapping & Avoidance

Geetesh Dubey, Ratnesh Madaan and Sebastian Scherer
Conference Paper, IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2018

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Wire detection, depth estimation, and avoidance is one of the hardest challenges towards the ubiquitous presence of robust autonomous aerial vehicles. We present an approach and a system which tackles these three challenges along with generic obstacle avoidance as well. First, we perform monocular wire detection using a convolutional neural network under the semantic segmentation paradigm, and obtain a confidence map of wire pixels. Along with this, we also use a binocular stereo pair to detect other generic obstacles. We represent wires and generic obstacles using a disparity space representation and do a C-space expansion by using a non-linear sensor model we develop. Occupancy inference for collision checking is performed by maintaining a pose graph over multiple disparity images. For avoidance of wire and generic obstacles, we use a precomputed trajectory library, which is evaluated in an online fashion in accordance to a cost function over proximity to the goal. We follow this trajectory with a path tracking controller. Finally, we demonstrate the effectiveness of our proposed method in simulation for wire mapping, and on hardware by multiple runs for both wire and generic obstacle avoidance.

author = {Geetesh Dubey, Ratnesh Madaan, Sebastian Scherer},
title = {DROAN – Disparity-space Representation for Obstacle AvoidaNce: Enabling Wire Mapping & Avoidance},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2018},
month = {October},
keywords = {UAV, wire, detection, reconstruction, avoidance, droan, disparity, obstacle, avoidance, drone, quadrotor, power, line},
} 2018-09-02T13:20:12-04:00