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- W2908852893 abstract "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." @default.
- W2908852893 created "2019-01-25" @default.
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- W2908852893 date "2018-10-01" @default.
- W2908852893 modified "2023-09-27" @default.
- W2908852893 title "DROAN - Disparity-Space Representation for Obstacle Avoidance: Enabling Wire Mapping & Avoidance" @default.
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- W2908852893 doi "https://doi.org/10.1109/iros.2018.8593499" @default.
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