Challenges in Visual Anomaly Detection for Mobile Robots
Not on the map. Why?
Abstract (arXiv)
We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 22 Sep 2022.
Builds on
4 of 11 references.
References · 4
- 2016Density estimation using Real NVPcited by 2,042
- 2018Deep Anomaly Detection with Outlier Exposurecited by 942
- 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
- 2018Anomaly Detection using Deep Learning based Image Completioncited by 27
Built on by
Citing papers per year: 2023: 1.
1 paper.