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

Prospect of naturally derived polysaccharides in intervention in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are chronic developmental brain disorders that can affect cognition, motor, social adaptation, behavior and so on due to multiple genetic or acquired causes. Natural polysaccharides are synthesized by living organisms, located in the cell wall, inside and between cells, and outside the cells, and are essential components of life activities. Previous studies have found that natural polysaccharides play an important role in neurological diseases, which mainly ameliorate the behavioral abnormalities and clinical symptoms caused by anti-oxidative stress, anti-neuronal apoptosis, anti-neuroinflammation, anti-excitatory amino acid toxicity, and regulation of the brain-gut axis. This review summarizes the intervention role of 17 bioactive polysaccharides from plants and fungi in neurological diseases, aiming to provide new ideas for the research and treatment of NDDs

Clinical and Radiological Outcomes of Cervical Disc Arthroplasty in Patients with Modic Change

Objective Modic change (MC) is defined as abnormalities observed in the intervertebral disc subchondral and adjacent vertebral endplate subchondral bone changes. Most studies on MC were reported in the lumbar spine and associated with lower back pain. However, MC has been rarely reported in the cervical spine, let alone in those who underwent cervical disc replacement (CDR). This study aimed to focus on MC in the cervical spine and reveal clinical and radiological parameters, especially heterotopic ossification (HO), for patients who underwent CDR. Furthermore, we illustrated the association between MC and HO. Methods We retrospectively reviewed patients who underwent CDA from January 2008 to December 2019. The Japanese Orthopaedic Association (JOA), Neck Disability Index (NDI), and Visual Analog Scale (VAS) scores were used to evaluate the clinical outcomes. Radiological evaluations were used to conclude the cervical alignment (CL) and range of motion (ROM) of C2‐7, functional spinal unit angle (FSUA), shell angle (SA), FSU height, and HO. Univariate and multivariate logistic regressions were performed to identify the risk factors for HO. The Kaplan–Meier (K‐M) method was used to analyze potential risk factors, and multivariate Cox regression was used to identify independent risk factors. Results A total of 139 patients were evaluated, with a mean follow‐up time of 46.53 ± 26.60 months. Forty‐nine patients were assigned to the MC group and 90 to the non‐MC group. The incidence of MC was 35.3%, with type 2 being the most common. Clinical outcomes (JOA, NDI, VAS) showed no significant difference between the two groups. The differences in C2‐7 ROM between the two groups were not significant, while the differences in SA ROM and FSUA ROM were significantly higher in the non‐MC than in the MC group (p < 0.05). Besides, FSU height in MC group was significantly lower than that in non‐MC group. Parameters concerning CL, including C2‐7, FSUA, SA, were not significantly different between the two groups. The incidence of HO and high‐grade HO, respectively, in the MC group was 83.7% and 30.6%, while that in the non‐MC group was 53.3% and 2.2%, and such differences were significant (p < 0.05). Multivariate logistic regression analyses and Cox regression showed that MC and involved level were significantly associated with HO occurrence (p < 0.05). No implant migration and secondary surgery were observed. Conclusion MC mainly affected the incidence of HO. Preoperative MC was significantly associated with HO formation after CDR and should be identified as a potential risk factor for HO. Rigorous criteria for MC should be taken into consideration when selecting appropriate candidates for CDR

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