A loop closure module plays an important role in visual SLAM systems, which can reduce the accumulat-ed drift. This task faces the challenges of large viewpoint changes and expensive computational costs when optimizing the global map. This paper proposes DH-LC, a novel accurate and robust loop closure method that consists of hierarchical spatial feature matching (HSFM) and hybrid bundle adjustment (HBA). HSFM estimates a reliable relative pose between the query image and the retrieval image in a coarse-to-fine way. Specifically, 3D points are firstly triangulated and then clus-tered according to the spatial distribution. The cluster centers estimate coarse cube-level matching pairs in a larger perception field which can tolerate large viewpoint changes. HBA optimizes the global map efficiently by adaptively selecting incremental bundle adjustment or full bundle adjustment according to the accumulated drift and relative pose verification in the temporal window. Experimental results demonstrate that our proposed method easily detects loops in large viewpoint changes and efficiently optimizes the global map. When compared with the state-of-the-art methods, our method increases loop closure recall and improves SLAM localization accuracy with reducing the accumulated drift.

Recent studies have shown that cascade cost volume can play a vital role in deep stereo matching to achieve high resolution depth map with efficient hardware usage. However, how to construct good cascade volume as well as effective sampling for them are still under in-depth study. Previous cascade-based methods usually perform uniform sampling in a predicted disparity range based on variance, which easily misses the ground truth disparity and decreases disparity map accuracy. In this paper, we propose an uncertainty adaptive sampling network (UASNet) featuring two modules: an uncertainty distribution-guided range prediction (URP) model and an uncertainty-based disparity sampler (UDS) module. The URP explores the more discriminative uncertainty distribution to handle the complex matching ambiguities and to improve disparity range prediction. The UDS adaptively adjusts sampling interval to localize disparity with improved accuracy. With the proposed modules, our UASNet learns to construct cascade cost volume and predict full-resolution disparity map directly. Extensive experiments show that the proposed method achieves the highest ground truth covering ratio compared with other cascade cost volume based stereo matching methods. Our method also achieves top performance on both SceneFlow dataset and KITTI benchmark.

Most of the state-of-the-art visual inertial SLAM methods pay less attention to 2D-2D and 3D-2D matching with more reliable features in a long time span, which easily results in continuous estimation drift. In this paper, we propose an efficient drift-free visual-inertial SLAM method by a pose guided feature matching method to re-identify existing features from a spatial-temporal sensitive sub-global map. The re-identified features serve as augmented visual measurements to anchor the current frame and gradually decrease the accumulated error in the long run. When incorporating the measurements into the optimization module, it benefits to build a drift-free global map in the system. Extensive experiments show that our feature re-identification method is both effective and efficient. Specifically, when combining the feature re-identification with the state-of-the-art SLAM method [1], our method achieves 67.3% and 87.5% absolute trajectory error reduction with only a small additional computational cost on two public SLAM benchmark DBs: EuRoC and TUM-VI respectively.

Most of the state-of-the-art visual-inertial SLAM methods pay less attention to the scene structure of man-made environments. In this paper, based on the assumption of multiple local Manhattan worlds (MWs), we propose a Manhattan frame (MF) re-identification method to build relative rotation constraints between MF matching pairs and tightly couple these constraints into global bundle adjust module. Specifically, a coarse-to-fine vanishing point (VP) estimation method and pose guided MF temporal consistency verification method are firstly proposed to improve the accuracy and robustness of MF estimation. Then unreliable MF matching pairs are filtered out by a spatial temporal consistency check. Finally, the relative rotation constraints of the remaining MF matching pairs are combined into global bundle adjustment energy function for further optimization. We have validated our proposed method on both synthetic and real-world datasets. When comparing with the baseline method [1], the real-time absolute trajectory error (ATE) of our proposed method has decreased by 29.1%, 19.8% on TartanAir hospital and EuRoC datasets respectively. Our method also exceeds existing state-of-the-art algorithms on both synthetic and real-world datasets.

Minimum Variance (MV) beamforming has been studied for high resolution ultrasonic imaging. However, it is not easy for the MV beamformer to be implemented into a real time diagnostic system, because it requires too many computations for covariance matrix inversion. We introduce a transform-based adaptive beamforming algorithm for ultrasound medical imaging where the transformation can reduce the dimension of the covariance matrix in estimating beamformation weights. Moreover, it is shown that beamformation result can be directly calculated in the transformed domain. Experimental results indicate that our beamforming method shows better resolution and contrast under a real phantom and in-vivo environment.

This paper proposes a method for estimating the illuminant chromaticity using the distributions of camera responses obtained by a CCD camera in a real-world scene. Illuminant estimation using a highlight method is based on the geometric relation between a body and its surface reflection. In general, the pixels in a highlight region are affected by an illuminant geometric difference, camera quantization errors and the nonuniformity of the CCD sensor. As such, this leads to inaccurate results if an illuminant is estimated using the pixels of a CCD camera without any preprocessing. Accordingly, to solve this problem, the proposed method analyzes the distribution of the CCD camera responses and selects pixels using the Mahalanobis distance in highlight regions. The use of the Mahalanobis distance based on the camera responses enables the adaptive selection of valid pixels among the pixels distributed in the highlight regions. Lines are then determined based on the selected pixels with r-g chromaticity coordinates using a principal component analysis (PCA). Thereafter, the illuminant chromaticity is estimated based on the intersection points of the lines. Experimental results using the proposed method demonstrated a reduced estimation error compared with the conventional method.