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Crash course learning: an automated approach to simulation-driven LiDAR-based training of neural networks for obstacle avoidance in mobile robotics

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This paper proposes and implements a self-supervised simulation-driven approach to data collection used for training of perception-based shallow neural networks for mobile robot obstacle avoidance. In the approach, a 2D LiDAR sensor was used as an information source for training neural networks. The paper analyzes neural network performance in terms of numbers of layers and neurons, as well as the amount of data needed for reliable robot operation.
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Crash course learning: an automated approach to simulation-driven LiDAR-based training of neural networks for obstacle avoidance in mobile robotics

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