Learning an Image-based Obstacle Detector With Automatic Acquisition of Training Data

Authors

  • Stefano Toniolo Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano
  • Jérôme Guzzi Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano
  • Luca Gambardella Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano
  • Alessandro Giusti Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano

Abstract

We detect and localize obstacles in front of a mobile robot by means of a deep neural network that maps images acquired from a forward-looking camera to the outputs of five proximity sensors. The robot autonomously acquires training data in multiple environments; once trained, the network can detect obstacles and their position also in unseen scenarios, and can be used on different robots, not equipped with proximity sensors. We demonstrate both the training and deployment phases on a small modified Thymio robot.

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Published

2018-04-29

How to Cite

Toniolo, S., Guzzi, J., Gambardella, L., & Giusti, A. (2018). Learning an Image-based Obstacle Detector With Automatic Acquisition of Training Data. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11368