LocNet: Global localization in 3D point clouds for mobile vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.Comment: 6 pages, IV 2018 accepte

Communication constrained cloud-based long-term visual localization in real time

Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloud-based visual localization system targeting at long-term localization in real time. On the robot, we employ two estimators to achieve accurate and real-time performance. One is a sliding-window based visual inertial odometry, which integrates constraints from consecutive observations and self-motion measurements, as well as the constraints induced by localization on the cloud. This estimator builds a local visual submap as the virtual observation which is then sent to the cloud as new localization constraints. The other one is a delayed state Extended Kalman Filter to fuse the pose of the robot localized from the cloud, the local odometry and the high-frequency inertial measurements. On the cloud, we propose a longer sliding-window based localization method to aggregate the virtual observations for larger field of view, leading to more robust alignment between virtual observations and the map. Under this architecture, the robot can achieve drift-free and real-time localization using onboard resources even in a network with limited bandwidth, high latency and existence of package loss, which enables the autonomous navigation in real-world environment. We evaluate the effectiveness of our system on a dataset with challenging seasonal and illuminative variations. We further validate the robustness of the system under challenging network conditions

Leveraging BEV Representation for 360-degree Visual Place Recognition

This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task

Risk Factors of Nonfusion after Anterior Cervical Decompression and Fusion in the Early Postoperative Period: A Retrospective Study

Objective Although high fusion rates have been reported for anterior cervical decompression and fusion (ACDF) in the medium and long term, the risk of nonfusion in the early period after ACDF remains substantial. This study investigates early risk factors for cage nonfusion in patients undergoing single‐ or multi‐level ACDF. Methods This was a retrospective study. From August 2020 to December 2021, 107 patients with ACDF, including 197 segments, were enrolled, with a follow‐up of 3 months. Among the 197 segments, 155 were diagnosed with nonfusion (Nonfusion group), and 42 were diagnosed with fusion (Fusion group) in the early period after ACDF. We assessed the significance of the patient‐specific factors, radiographic indicators, serum factors, and clinical outcomes. The Wilcoxon rank sum test, t‐tests, analysis of variance, and stepwise multivariate logistic regression were used for statistical analysis. Results Univariate analysis showed that smoking, insufficient improvement in the C2‐7 Cobb angle (p = 0.024) and the functional spinal unit Cobb angle (p = 0.022) between preoperative and postoperative stages and lower serum calcium (fusion: 2.34 ± 0.12 mmol/L; nonfusion: 2.28 ± 0.17 mmol/L, p = 0.003) β‐carboxyterminal telopeptide end of type 1 collagen (β‐CTX) (fusion: 0.51 [0.38, 0.71]; nonfusion: 0.43 [0.31, 0.57], p = 0.008), and N‐terminal fragment of osteocalcin (N‐MID‐BGP) (fusion: 18.30 [12.15, 22.60]; nonfusion: 14.45 [11.65, 18.60], p = 0.023) are risk factors for nonfusion in the early period after ACDF. Stepwise logistic regression analysis revealed that poor C2‐7 Cobb angle improvement (odds ratio [OR], 1.107 [1.019–1.204], p = 0.017) and lower serum calcium (OR, 3.700 [1.138–12.032], p = 0.030) are risk factors. Conclusions Patients with successful fusion after ACDF had higher preoperative serum calcium and improved C2‐7 Cobb angle than nonfusion patients at 3 months. These findings suggest that serum calcium could be used to identify patients at risk of nonfusion following ACDF and that correcting the C2‐7 Cobb angle during surgery could potentially increase fusion in the early period after ACDF

New Insights into the Inhibition Mechanism of Betulinic Acid on α‑Glucosidase

Betulinic acid (BA), an important pentacyclic triterpene widely distributed in many foods, possesses high antidiabetic activity. In this study, BA was found to exhibit stronger inhibition of α-glucosidase than acarbose with an IC₅₀ value of (1.06 ± 0.02) × 10^–5 mol L^–1 in a mixed-type manner. BA bound with α-glucosidase to form a BA−α-glucosidase complex, resulting in a more compact structure of the enzyme. The obtained concentrations and spectra profiles of the components resolved by the multivariate-curve resolution–alternating least-squares confirmed the formation of the BA−α-glucosidase complex. Molecular docking showed that BA tightly bound to the active cavity of α-glucosidase, which might hinder the entrance of the substrate leading to a decline in enzyme activity. The chemical modification of α-glucosidase verified the results of the computer simulation that the order of importance of the four amino acid residues in the binding process was His > Tyr > Lys > Arg