Uncertainty-aware occupancy map prediction using generative networks for robot navigation

Uncertainty-aware occupancy map prediction using generative networks for robot navigation. Katyal, K., Popek, K., Paxton, C., Burlina, P., & Hager, G., D. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May:5453-5459, 2019.

Paper doi abstract bibtex

Efficient exploration through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. We present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We develop a custom loss function used to make accurate prediction while emphasizing physical boundaries. We further study extensions to our neural network architecture to account for uncertainty and ambiguity inherent in mapping and exploration. Finally, we demonstrate a combined map prediction and information-theoretic exploration strategy using the variance of the generated hypotheses as the heuristic for efficient exploration of unknown environments.

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 title = {Uncertainty-aware occupancy map prediction using generative networks for robot navigation},
 type = {article},
 year = {2019},
 pages = {5453-5459},
 volume = {2019-May},
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 abstract = {Efficient exploration through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. We present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We develop a custom loss function used to make accurate prediction while emphasizing physical boundaries. We further study extensions to our neural network architecture to account for uncertainty and ambiguity inherent in mapping and exploration. Finally, we demonstrate a combined map prediction and information-theoretic exploration strategy using the variance of the generated hypotheses as the heuristic for efficient exploration of unknown environments.},
 bibtype = {article},
 author = {Katyal, Kapil and Popek, Katie and Paxton, Chris and Burlina, Phil and Hager, Gregory D.},
 doi = {10.1109/ICRA.2019.8793500},
 journal = {Proceedings - IEEE International Conference on Robotics and Automation}
}

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