Sensing Anomalies as Potential Hazards: Datasets and Benchmarks

• URL: http://arxiv.org/abs/2110.14706v1
• Date: Wed, 27 Oct 2021 18:47:06 GMT
• Title: Sensing Anomalies as Potential Hazards: Datasets and Benchmarks
• Authors: Dario Mantegazza (1), Carlos Redondo (2), Fran Espada (2), Luca M. Gambardella (1), Alessandro Giusti (1) and J\'er\^ome Guzzi (1) ((1) Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Lugano, Switzerland,(2) Hovering Solutions Ltd, Madrid, Spain)
• Abstract summary: We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual. We contribute three novel image-based datasets acquired in robot exploration scenarios. We study the performance of an anomaly detection approach based on autoencoders operating at different scales.
• Score: 43.55994393060723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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