Abstract
As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises important questions concerning the safety and reliability of learning-based perception systems. There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time. However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic. In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature in the face of this challenge. This paper attempts to identify these trends and summarize the various approaches to the topic.
Highlights
Deep Neural Networks (DNNs) show impressive results on many computer vision tasks such as image classification [1], object detection [2], depth estimation [3] and semantic segmentation [4]
A growing body of research is showing that stateof-the-art DNNs suffer a drop in performance when tested on data that differs from their training and testing sets [9]–[11]
This fact is of particular importance for deep learning based robotic perception since a robot may experience a wide range of environmental conditions that were not represented in the training data
Summary
Deep Neural Networks (DNNs) show impressive results on many computer vision tasks such as image classification [1], object detection [2], depth estimation [3] and semantic segmentation [4]. This paper identifies and discusses emerging research trends that address the run-time performance monitoring of the learning-based components in autonomous robotic systems.
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