Retrievalbased Localization Based on Domaininvariant Feature Learning under Changing Environments Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dynamic objects), retrievalbased localization is severely hampered and becomes a challenging problem. In this paper, a novel domaininvariant feature learning method (DIFL) is proposed based on ComboGAN, a multidomain image translation network architecture. By introducing a feature consistency loss (FCL) between the encoded features of the original image and translated image in another domain, we are able to train the encoders to generate domaininvariant features in a selfsupervised manner. To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domaininvariant feature vector. We then preform retrieval by selecting the database image with the most similar domaininvariant feature vector. We validate the proposed approach on the CMUSeasons dataset, where we outperform stateoftheart learningbased descriptors in retrievalbased localization for high and medium precision scenarios.

Learning Local Feature Descriptor with Motion Attribute For Visionbased Localization In recent years, camerabased localization has been widely used for robotic applications, and most proposed algorithms rely on local features extracted from recorded images. For better performance, the features used for openloop localization are required to be shortterm globally static, and the ones used for relocalization or loop closure detection need to be longterm static. Therefore, the motion attribute of a local feature point could be exploited to improve localization performance, e.g., the feature points extracted from moving persons or vehicles can be excluded from these systems due to their unsteadiness. In this paper, we design a fully convolutional network (FCN), named MDNet, to perform motion attribute estimation and feature description simultaneously. MDNet has a shared backbone network to extract features from the input image and two network branches to complete each subtask. With MDNet, we can obtain the motion attribute while avoiding increasing much more computation. Experimental results demonstrate that the proposed method can learn distinct local feature descriptor along with motion attribute only using an FCN, by outperforming competing methods by a wide margin. We also show that the proposed algorithm can be integrated into a visionbased localization algorithm to improve estimation accuracy significantly.