  In this work we propose a new CNN+LSTM architecture for camera pose
regression for indoor and outdoor scenes. CNNs allow us to learn suitable
feature representations for localization that are robust against motion blur
and illumination changes. We make use of LSTM units on the CNN output, which
play the role of a structured dimensionality reduction on the feature vector,
leading to drastic improvements in localization performance. We provide
extensive quantitative comparison of CNN-based and SIFT-based localization
methods, showing the weaknesses and strengths of each. Furthermore, we present
a new large-scale indoor dataset with accurate ground truth from a laser
scanner. Experimental results on both indoor and outdoor public datasets show
our method outperforms existing deep architectures, and can localize images in
hard conditions, e.g., in the presence of mostly textureless surfaces, where
classic SIFT-based methods fail.
