  $ $Visual place recognition is challenging, especially when only a few place
exemplars are given. To mitigate the challenge, we consider place recognition
method using omnidirectional cameras and propose a novel Omnidirectional
Convolutional Neural Network (O-CNN) to handle severe camera pose variation.
Given a visual input, the task of the O-CNN is not to retrieve the matched
place exemplar, but to retrieve the closest place exemplar and estimate the
relative distance between the input and the closest place. With the ability to
estimate relative distance, a heuristic policy is proposed to navigate a robot
to the retrieved closest place. Note that the network is designed to take
advantage of the omnidirectional view by incorporating circular padding and
rotation invariance. To train a powerful O-CNN, we build a virtual world for
training on a large scale. We also propose a continuous lifted structured
feature embedding loss to learn the concept of distance efficiently. Finally,
our experimental results confirm that our method achieves state-of-the-art
accuracy and speed with both the virtual world and real-world datasets.
