Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem. In contrast, we present a VO framework by incorporating two additional components called Memory and Refining. The Memory component preserves global information by employing an adaptive and efficient selection strategy. The Refining component ameliorates previous results with the contexts stored in the Memory by adopting a spatial-temporal attention mechanism for feature distilling. Experiments on the KITTI and TUM-RGBD benchmark datasets demonstrate that our method outperforms state-of-the-art learning-based methods by a large margin and produces competitive results against classic monocular VO approaches. Especially, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic VO algorithms tend to fail.

Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data makes them difficult to generalize to new scenes. In this paper, we propose an online adaptation framework for deep VO with the assistance of scene-agnostic geometric computations and Bayesian inference. In contrast to learning-based pose estimation, our method solves pose from optical flow and depth while the single-view depth estimation is continuously improved with new observations by online learned uncertainties. Meanwhile, an online learned photometric uncertainty is used for further depth and pose optimization by a differentiable Gauss-Newton layer. Our method enables fast adaptation of deep VO networks to unseen environments in a self-supervised manner. Extensive experiments including Cityscapes to KITTI and outdoor KITTI to indoor TUM demonstrate that our method achieves state-of-the-art generalization ability among self-supervised VO methods.

We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses from image pairs by minimizing photometric loss between warped and captured images. As single-view depth estimation is an ill-posed problem, and photometric loss is incapable of discriminating distortion artifacts of warped images, the estimated depth is vague and pose is inaccurate. In contrast to previous methods, our framework learns a compact representation of frame-to-frame correlation, which is updated by incorporating sequential information. The updated representation is used for depth estimation. Besides, we tackle VO as a self-supervised image generation task and take advantage of Generative Adversarial Networks (GAN). The generator learns to estimate depth and pose to generate a warped target image. The discriminator evaluates the quality of generated image with high-level structural perception that overcomes the problem of pixel-wise loss in previous methods. Experiments on KITTI and Cityscapes datasets show that our method obtains more accurate depth with details preserved and predicted pose outperforms state-of-theart self-supervised methods significantly.

In view of the performance requirements of mass ultra-high performance concrete (UHPC) for the Pang Gong bridge steel cable tower in China, the UHPC incorporating of steel slag powder and hybrid expansive agents is optimized and prepared. The effects of steel slag powder and hybrid expansive agents on the hydration characteristics and persistent shrinkage of UHPC are investigated. The results indicate that 15 wt.% steel slag powder and 5 wt.% hybrid expansive agents can effectively reduce the drying shrinkage deformation of UHPC with a slight decrease of strength. Heat flow calorimetry results show that the incorporation of steel slag powder and expansive agents decreases the hydration heat at three days. Moreover, the obtained adiabatic temperature rise of UHPC is 59.5 °C and the total shrinkage value at 180 days is 286 με. The hydration heat release changes of large volume UHPC in the steel-concrete section of cable tower is agreed with the result of adiabatic temperature rise in the laboratory.

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By capturing a set of low-resolution images under different illumination angles and stitching them together in the Fourier domain, Fourier ptychography (FP) is capable of providing highresolution image with large field of view. Despite its validity, long acquisition time limits its real-time application. We proposed an incomplete sampling scheme in this paper, termed the predictive searching algorithm to shorten the acquisition and recovery time. Informative subregions of the sample's spectrum are searched and the corresponding images of the most informative directions are captured for spectrum expansion. Its effectiveness is validated by both simulated and experimental results, whose data requirement is reduced by ∼64% to ∼90% without sacrificing image reconstruction quality compared with the conventional FP method.

Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality. Feed-forward absolute camera pose regression methods directly output poses by a network, but suffer from low accuracy. Meanwhile, scene coordinate based methods are accurate, but need iterative RANSAC post-processing, which brings challenges to efficient end-to-end training and inference. In order to have the best of both worlds, we propose a feed-forward method termed SC-wLS that exploits all scene coordinate estimates for weighted least squares pose regression. This differentiable formulation exploits a weight network imposed on 2D-3D correspondences, and requires pose supervision only. Qualitative results demonstrate the interpretability of learned weights. Evaluations on 7Scenes and Cambridge datasets show significantly promoted performance when compared with former feed-forward counterparts. Moreover, our SC-wLS method enables a new capability: self-supervised test-time adaptation on the weight network. Codes and models are publicly available.