Visual odometry (VO) plays a vital role in Simultaneous Localization and Mapping (SLAM). Most of the current learning-based VO methods utilize Convolutional Neural Network (CNN) as framework. However, CNN is weak in integrating global context information. In the design of loss function, the majority of these methods ignore the restraint relationship between translation and rotation prediction. In this work we propose an end-to-end global-context-aware visual odometry scheme with Epipolar-Geometry-constrained loss function to estimate the relative 6-DoF poses of monocular camera. The proposed scheme designs an augmented-attention-enhanced global context block on top of contextual CNN to learn the long-range dependencies and internal correlation. To overcome the problem of mutual restraint between the translation and rotation errors, an Epipolar-Geometry-constrained loss function is developed to simultaneously improve the prediction accuracy of both translation and rotation. The evaluation results on public datasets and self-collected dataset show that the proposed system outperforms state-of-the-art learning-based VO methods with a large margin.

Loop closure detection is an important component of Simultaneous Localization and Mapping (SLAM). In this paper, a novel two-branch loop closure detection algorithm unifying deep Convolutional Neural Network (ConvNet) features and semantic edge features is proposed. In detail, we use one feature extraction module to extract both ConvNet and semantic edge features simultaneously. The deep ConvNet features are subjected to a Context Feature Enhancement (CFE) module in the global feature ranking branch to generate a representative global feature descriptor. Concurrently, to reduce the interference of dynamic features, the extracted semantic edge information of landmarks is encoded through the Vector of Locally Aggregated Descriptors (VLAD) framework in the semantic edge feature ranking branch to form semantic edge descriptors. Finally, semantic, visual, and geometric information is integrated by the similarity score fusion calculation. Extensive experiments on six public datasets show that the proposed approach can achieve competitive recall rates at 100% precision compared to other state-of-the-art methods.