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2024INF010
Doctoral thesis

Hazard detection for robotic applications as visual anomaly detection

Università della Svizzera italiana

  • 2024

PhD: Università della Svizzera italiana

Anomaly detection
Computer vision
Robotics
Deep learning
Artificial intelligence
Hazard detection
English For a robot, a hazard is an event or object that poses risks to its mission or to itself. Some hazards such as obstacles are known, and can be accounted for; others, such as piercing debris or dense fog might be unexpected and may be seen only on some rare occasions. For these, collecting samples to train a perception model is often impossible. Thus, a hazard detection system should not require hazard samples to function correctly. In this thesis, we propose to use deep learning-based visual anomaly detection models to solve hazard detection for mobile robots employed in industry. Our proposal of relying on visual anomaly detection is particularly suited for these robots since most of those have cameras. Anomaly detection is a machine learning topic focused on finding rare, unexpected, patterns in data that deviate from an expected behavior. It can be applied to various fields and data types, but the application of anomaly detection in robotics is rather new and limited to specific use cases. Nonetheless, anomaly detection fits well with hazard detection as it requires datasets composed only of non-anomalous (i.e., expected, normal) samples. No public datasets are available for the task of hazard detection for robotics. We start by closing this gap with our general-purpose visual hazard detection dataset for mobile robots. Then, we introduce a hazard detection system based on convolutional undercomplete autoencoders. Our approach detects multiple types of hazards using only images coming from the robot's front-facing camera. We test this solution using two real-world qualitative demonstrations with a wheeled robot in a lab, and an industrial drone in a factory, and detect all anomalies. In both cases, all anomalies are detected. Based on the expectation that few anomalous samples will be collected during deployment, we experiment with an outlier exposure approach, to learn from these key anomalous samples. We employ a Real-NVP model, combined with a features extractor and a novel loss, to train using a few detected anomalies in addition to normal samples. Our experiments show that our solution effectively increases the detection performance for all anomalies, measured by the AUC, by 9.6%. Similarly, we can expect that the data collected by the deployed robots becomes too much to be all manually inspected and labeled. We propose two novel active learning methods designed for anomaly detection using Real-NVP. We test our solutions against six other queries strategies from the literature, across more than 6500 experiments. We show that when multiple samples are collected, our approaches are best for choosing informative samples collected. Lastly, we study how pre-trained feature extraction models perform on 3D anomaly detection tasks. Our results show that while our approaches are better than older models and baselines when data is scarce, ad hoc models outperform our proposed solution when enough data is available.
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Language
  • English
Classification
Computer science and technology
License
License undefined
Open access status
green
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Persistent URL
https://n2t.net/ark:/12658/srd1329031
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