Dataset for Sensing Anomalies as Potential Hazards in Mobile Robots

Description

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.  

Resources

Name Format Description Link
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7035788
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7035788
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7035788
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7035788
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7035788
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7035788

Tags

  • anomaly-detection
  • deep-learning
  • out-of-distribution-detection
  • robot-vision

Topics

Categories