  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 that networks trained
for semantic place categorization also perform better at (specific) place
recognition when faced with severe appearance changes and provide a reference
for which networks and layers are optimal for different aspects of the place
recognition problem.
