On the Performance of ConvNet Features for Place Recognition

Understand

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.