Challenges in Visual Anomaly Detection for Mobile Robots
This work addresses visual anomaly detection for mobile robots, but it appears incremental as it focuses on dataset creation and testing existing methods.
The paper tackled the problem of detecting visual anomalies for autonomous mobile robots by categorizing anomaly types and testing a state-of-the-art unsupervised deep learning method on a novel dataset, with results discussed for real-world deployment.
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.