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This paper presents a semi-supervised framework for multi-level description learning aiming for robust and accurate camera relocalization across large perception variations. Our proposed network, namely DLSSNet, simultaneously learns weakly-supervised semantic segmentation and local feature description in the hierarchy. Therefore, the augmented descriptors, trained in an end-to-end manner, provide a more stable high-level representation for local feature dis-ambiguity. To facilitate end-to-end semantic description learning, the descriptor segmentation module is proposed to jointly learn semantic descriptors and cluster centers using standard semantic segmentation loss. We show that our model can be easily fine-tuned for domain-specific usage without any further semantic annotations, instead, requiring only 2D-2D pixel correspondences. The learned descriptors, trained with our proposed pipeline, can boost the cross-season localization performance against other state-of-the-arts. 

This work describes a monocular visual odometry framework, which exploits the best attributes of edge features for illumination-robust camera tracking, while at the same time ameliorating the performance degradation of edge mapping. In the front-end, an ICP-based edge registration provides robust motion estimation and coarse data association under lighting changes. In the back-end, a novel edge-guided data association pipeline searches for the best photometrically matched points along geometrically possible edges through template matching, so that the matches can be further refined in later bundle adjustment. The core of our proposed data association strategy lies in a point-to-edge geometric uncertainty analysis, which analytically derives (1) a probabilistic search length formula that significantly reduces the search space and (2) a geometric confidence metric for mapping degradation detection based on the predicted depth uncertainty. Moreover, a match confidence based patch size adaption strategy is integrated into our pipeline to reduce matching ambiguity. We present extensive analysis and evaluation of our proposed system on synthetic and real- world benchmark datasets under the influence of illumination changes and large camera motions, where our proposed system outperforms current state-of-art algorithms.