# Challenges in Visual Anomaly Detection for Mobile Robots

arXiv: https://arxiv.org/abs/2209.10995 · HTML: https://mlsanity.com/paper/2209.10995/

## Key facts

- Cited by 1 papers 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 (most cited first, 4 of 4)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Anomaly Detection using Deep Learning based Image Completion](https://mlsanity.com/paper/1811.06861/) (2018)

## Built on by (most cited first, 1 of 1)

- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)

## 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.