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Sensing Anomalies as Potential Hazards: Datasets and Benchmarks

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Towards Autonomous Robotic Systems (TAROS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13546))

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Abstract

We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot’s previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies. On these datasets, we study the performance of an anomaly detection approach based on autoencoders operating at different scales.

This work was supported as a part of NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543) and by the European Commission through the Horizon 2020 project 1-SWARM, grant ID 871743.

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Notes

  1. 1.

    Similarly, retention of information following encounters with novel predators is one of the recognized evolutionary advantages of neophobic animals [21].

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Authors and Affiliations

  1. Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Lugano, Switzerland

    Dario Mantegazza, Luca M. Gambardella, Alessandro Giusti & Jérôme Guzzi

  2. Hovering Solutions Ltd., Madrid, Spain

    Carlos Redondo & Fran Espada

Authors
  1. Dario Mantegazza
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  2. Carlos Redondo
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  3. Fran Espada
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  4. Luca M. Gambardella
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  5. Alessandro Giusti
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  6. Jérôme Guzzi
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Correspondence to Dario Mantegazza .

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  1. UKAEA's RACE, Abingdon, UK

    Salvador Pacheco-Gutierrez

  2. UKAEA's RACE, Abingdon, UK

    Alice Cryer

  3. UKAEA's RACE, Abingdon, UK

    Ipek Caliskanelli

  4. UKAEA's RACE, Abingdon, UK

    Harun Tugal

  5. UKAEA's RACE, Abingdon, UK

    Robert Skilton

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Mantegazza, D., Redondo, C., Espada, F., Gambardella, L.M., Giusti, A., Guzzi, J. (2022). Sensing Anomalies as Potential Hazards: Datasets and Benchmarks. In: Pacheco-Gutierrez, S., Cryer, A., Caliskanelli, I., Tugal, H., Skilton, R. (eds) Towards Autonomous Robotic Systems. TAROS 2022. Lecture Notes in Computer Science(), vol 13546. Springer, Cham. https://doi.org/10.1007/978-3-031-15908-4_17

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  • DOI: https://doi.org/10.1007/978-3-031-15908-4_17

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