  This paper proposes a new image-based localization framework that explicitly
localizes the camera/robot by fusing Convolutional Neural Network (CNN) and
sequential images' geometric constraints. The camera is localized using a
single or few observed images and training images with 6-degree-of-freedom pose
labels. A Siamese network structure is adopted to train an image descriptor
network, and the visually similar candidate image in the training set is
retrieved to localize the testing image geometrically. Meanwhile, a
probabilistic motion model predicts the pose based on a constant velocity
assumption. The two estimated poses are finally fused using their uncertainties
to yield an accurate pose prediction. This method leverages the geometric
uncertainty and is applicable in indoor scenarios predominated by diffuse
illumination. Experiments on simulation and real data sets demonstrate the
efficiency of our proposed method. The results further show that combining the
CNN-based framework with geometric constraint achieves better accuracy when
compared with CNN-only methods, especially when the training data size is
small.
