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On the Performance of ConvNet Features for Place Recognition by Sünderhauf, Niko; Dayoub, Feras; Shirazi, Sareh; Upcroft, Ben; Milford, Michael is a scholarly article available to read on EtoBox.

What is On the Performance of ConvNet Features for Place Recognition about?

After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different. This paper comprehensively evaluates and compares the utility of three state-of-the-art ConvNets on the problems of particular relevance to navigation for robots; viewpoint-invariance and condition-invariance, and for the first time enables real-time place recognition performance using ConvNets with large maps by integrating a variety of existing (locality-sensitive hashing) and novel (semantic search space partitioning) optimization techniques. We present extensive experiments on four real world datasets cultivated to evaluate each of the specific challenges in place recognition. The results demonstrate that speed-ups of two orders of magnitude can be achieved with minimal accuracy degradation, enabling real-time performance. We confirm tha

Author

Sünderhauf, Niko; Dayoub, Feras; Shirazi, Sareh; Upcroft, Ben; Milford, Michael

Published

2015

Language

EN