Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients

• URL: http://arxiv.org/abs/2412.07565v1
• Date: Tue, 10 Dec 2024 14:56:47 GMT
• Title: Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients
• Authors: Simon Kristoffersson Lind, Rudolph Triebel, Volker Krüger,
• Abstract summary: Out-of-distribution detection is commonly proposed as a solution for ensuring reliability in real-world deployment. We show that our method achieves a 60% higher success rate for an object detection task compared to previous methods.
• Score: 14.475978805963267
• License:
• Abstract: Modern robotic perception is highly dependent on neural networks. It is well known that neural network-based perception can be unreliable in real-world deployment, especially in difficult imaging conditions. Out-of-distribution detection is commonly proposed as a solution for ensuring reliability in real-world deployment. Previous work has shown that normalizing flow models can be used for out-of-distribution detection to improve reliability of robotic perception tasks. Specifically, camera parameters can be optimized with respect to the likelihood output from a normalizing flow, which allows a perception system to adapt to difficult vision scenarios. With this work we propose to use the absolute gradient values from a normalizing flow, which allows the perception system to optimize local regions rather than the whole image. By setting up a table top picking experiment with exceptionally difficult lighting conditions, we show that our method achieves a 60% higher success rate for an object detection task compared to previous methods.

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