bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling.

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

CDK9 inhibition activates innate immune response through viral mimicry.

Cancer cells frequently exhibit hyperactivation of transcription, which can lead to increased sensitivity to compounds targeting the transcriptional kinases, in particular CDK9. However, mechanistic details of CDK9 inhibition-induced cancer cell-selective anti-proliferative effects remain largely unknown. Here, we discover that CDK9 inhibition activates the innate immune response through viral mimicry in cancer cells. In MYC over-expressing prostate cancer cells, CDK9 inhibition leads to the gross accumulation of mis-spliced RNA. Double-stranded RNA (dsRNA)-activated kinase can recognize these mis-spliced RNAs, and we show that the activity of this kinase is required for the CDK9 inhibitor-induced anti-proliferative effects. Using time-resolved transcriptional profiling (SLAM-seq), targeted proteomics, and ChIP-seq, we show that, similar to viral infection, CDK9 inhibition significantly suppresses transcription of most genes but allows selective transcription and translation of cytokines related to the innate immune response. In particular, CDK9 inhibition activates NFκB-driven cytokine signaling at the transcriptional and secretome levels. The transcriptional signature induced by CDK9 inhibition identifies prostate cancers with a high level of genome instability. We propose that it is possible to induce similar effects in patients using CDK9 inhibition, which, we show, causes DNA damage in vitro. In the future, it is important to establish whether CDK9 inhibitors can potentiate the effects of immunotherapy against late-stage prostate cancer, a currently lethal disease.

SLAM (CD150) receptor homologous peptides block the peste des petits ruminants virus entry into B95a cells.

The fusion of haemagglutinin-neuraminidase (HN) protein of peste des petits ruminant (PPR) virus with signaling lymphocyte activation molecules (SLAM) host cell receptor consequences the virus entry and multiplication inside the host cell. The use of synthetic SLAM homologous peptides (i.e., molecular decoy for HN protein of PPR virus) may check PPR infection at the preliminary stage. Hence, the predicted SLAM homologous peptides using bioinformatics tools were synthesized by solid phase chemistry with standard Merrifield's 9-fluorenylmethoxycarbonyl (Fmoc) chemistry and were purified by reverse phase high performance liquid chromatography. The secondary structures of synthesized peptides were elucidated by circular dichroism spectroscopy. The in vitro interactions of these peptides were studied through indirect Enzyme Linked Immuno Sorbent Assay (ELISA) and visual surface plasmon UV-visible spectroscopy. The SLAM homologous peptides were able to interact with the peste des petits ruminant virus (PPRV) with varying binding efficiency. The interaction of SLAM homologous peptide with the PPR virus was ascertained by the change in the plasmon color from red wine to purple during visual detection and also by bathochromic shift in absorbance spectra under UV-visible spectrophotometry. The cytotoxic and anti-PPRV effect of these peptides were also evaluated in B95a cell line using PPR virus (Sungri/96). The cytotoxic concentration 50 (CC50 ) value of each peptide was greater than 1000 µg mL-1 . The anti-PPRV efficiency of SLAM-22 was relatively high among SLAM homologous peptides, SLAM-22 at 25 µg mL-1 concentration showed a reduction of more than log10 3 virus titer by priming of B95a cell line while the use of SLAM-15 and Muco-17 at the same concentration dropped virus titer from log10 4.8 to log10 2.5 and log10 3.1 respectively. The concentration of SLAM homologous peptide (25 µg mL-1 ) to exert its anti-PPRV effect was much less than its CC50 level (>1000 µg mL-1 ). Therefore, the synthetic SLAM homologous peptides may prove to be better agents to target PPRV.

The Cytomegalovirus M35 Protein Directly Binds to the Interferon-ß Enhancer and Modulates Transcription of Ifnb1 and Other IRF3-Driven Genes.

Induction of type I interferon (IFN) gene expression is among the first lines of cellular defense a virus encounters during primary infection. We previously identified the tegument protein M35 of murine cytomegalovirus (MCMV) as an essential antagonist of this antiviral system, showing that M35 interferes with type I IFN induction downstream of pattern-recognition receptor (PRR) activation. Here, we report structural and mechanistic details of M35's function. Determination of M35's crystal structure combined with reverse genetics revealed that homodimerization is a key feature for M35's immunomodulatory activity. In electrophoretic mobility shift assays (EMSAs), purified M35 protein specifically bound to the regulatory DNA element that governs transcription of the first type I IFN gene induced in nonimmune cells, Ifnb1. DNA-binding sites of M35 overlapped with the recognition elements of interferon regulatory factor 3 (IRF3), a key transcription factor activated by PRR signaling. Chromatin immunoprecipitation (ChIP) showed reduced binding of IRF3 to the host Ifnb1 promoter in the presence of M35. We furthermore defined the IRF3-dependent and the type I IFN signaling-responsive genes in murine fibroblasts by RNA sequencing of metabolically labeled transcripts (SLAM-seq) and assessed M35's global effect on gene expression. Stable expression of M35 broadly influenced the transcriptome in untreated cells and specifically downregulated basal expression of IRF3-dependent genes. During MCMV infection, M35 impaired expression of IRF3-responsive genes aside of Ifnb1. Our results suggest that M35-DNA binding directly antagonizes gene induction mediated by IRF3 and impairs the antiviral response more broadly than formerly recognized. IMPORTANCE Replication of the ubiquitous human cytomegalovirus (HCMV) in healthy individuals mostly goes unnoticed but can impair fetal development or cause life-threatening symptoms in immunosuppressed or -deficient patients. Like other herpesviruses, CMV extensively manipulates its hosts and establishes lifelong latent infections. Murine CMV (MCMV) presents an important model system as it allows the study of CMV infection in the host organism. We previously showed that during entry into host cells, MCMV virions release the evolutionary conserved protein M35 protein to immediately dampen the antiviral type I interferon (IFN) response induced by pathogen detection. Here, we show that M35 dimers bind to regulatory DNA elements and interfere with recruitment of interferon regulatory factor 3 (IRF3), a key cellular factor for antiviral gene expression. Thereby, M35 interferes with expression of type I IFNs and other IRF3-dependent genes, reflecting the importance for herpesviruses to avoid IRF3-mediated gene induction.

A single-cell RNA labeling strategy for measuring stress response upon tissue dissociation.

Tissue dissociation, a crucial step in single-cell sample preparation, can alter the transcriptional state of a sample through the intrinsic cellular stress response. Here we demonstrate a general approach for measuring transcriptional response during sample preparation. In our method, transcripts made during dissociation are labeled for later identification upon sequencing. We found general as well as cell-type-specific dissociation response programs in zebrafish larvae, and we observed sample-to-sample variation in the dissociation response of mouse cardiomyocytes despite well-controlled experimental conditions. Finally, we showed that dissociation of the mouse hippocampus can lead to the artificial activation of microglia. In summary, our approach facilitates experimental optimization of dissociation procedures as well as computational removal of transcriptional perturbation response.

California sea lion (Zalophus californianus) lymph-node explant reveals involvement and possible transcriptional regulation of SLAM and nectin-4 during phocine distemper virus infection.

Phocine distemper virus (PDV) is a significant cause of mortality for phocid seals; however, the susceptibility of otariids to this virus is poorly understood. The authors used a lymph-node explant culture system from California sea lions (Zalophus californianus, CSL) to investigate: (1) the role of signaling lymphocyte activation molecule (SLAM) and nectin-4 in PDV infection and their cellular expression patterns, (2) if PDV induces transcriptional regulation of cell-entry receptors, and (3) the involvement of apoptosis in PDV infection. PDV replicated in the lymph-node explants with peak replication 3 days post-infection (dpi), but the replication was not sustained 4 to 5 dpi. The PDV+ cells co-localized SLAM and nectin-4. These cells expressed IBA1, indicating a histiocytic lineage. Comparison of receptor expression between infected and mock-infected lymph nodes suggested transcriptional downregulation of both receptors during the initial stage of infection and upregulation during the late stage of infection, but the values lack of statistical significance. Cleaved caspase-3+ cells were slightly increased in the infected lymph nodes compared with the mock-infected lymph node from 1 to 4 dpi, but without statistical significance, and a few apoptotic cells co-expressed PDV. The results suggest that lymph-node explants might be an important model to study PDV pathogenesis. CSLs have the potential to be infected with PDV, as they express both cell-entry receptors in histiocytes. The lack of statistical significance in the PDV replication, transcriptional regulation of viral receptors, and changes in apoptosis suggest that although CSL might be infected by PDV, they might be less susceptible than phocid species.

An object-oriented analysis of social apps, syringes and ARTs within gay Taiwanese men's chemsex practices.

Critical drug studies explore the discursive and material dimensions of sexualised drug use to overcome individualised and often pathologising notions such as risk, safety, responsibility and pleasure. This article uses an object-oriented approach-following the use and flow of social apps, syringes and antiretroviral therapy (ART)-to analyse gay and bisexual Taiwanese men's drug practices. Interview data from fourteen men are used to articulate how objects were brought into gay and bisexual men's chemsex repertoire in ways that shaped individuals' safe-sex communication, intimacy maintenance and stigma negotiation. An object-oriented approach scrutinises risk, pleasure and identities in assemblages of the human and nonhuman, and can help identify new opportunities for implementing health promotion interventions and policies.

A Visual-Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System.

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%.

Multimodal Mobile Robotic Dataset for a Typical Mediterranean Greenhouse: The GREENBOT Dataset.

This paper presents an innovative dataset designed explicitly for challenging agricultural environments, such as greenhouses, where precise location is crucial, but GNNS accuracy may be compromised by construction elements and the crop. The dataset was collected using a mobile platform equipped with a set of sensors typically used in mobile robots as it was moved through all the corridors of a typical Mediterranean greenhouse featuring tomato crops. This dataset presents a unique opportunity for constructing detailed 3D models of plants in such indoor-like spaces, with potential applications such as robotized spraying. For the first time, to the authors' knowledge, a dataset suitable to test simultaneous localization and mapping (SLAM) methods is presented in a greenhouse environment, which poses unique challenges. The suitability of the dataset for this purpose is assessed by presenting SLAM results with state-of-the-art algorithms. The dataset is available online.

A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR.

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and Ranging) is an expanding field of research with numerous applications in the areas of autonomous driving, mobile robotics, and UAVs (Unmanned Aerial Vehicles). However, in most real-world scenarios, dynamic objects can negatively impact the accuracy and robustness of SLAM. In recent years, the challenge of achieving optimal SLAM performance in dynamic environments has led to the emergence of various research efforts, but there has been relatively little relevant review. This work delves into the development process and current state of SLAM based on 3D LiDAR in dynamic environments. After analyzing the necessity and importance of filtering dynamic objects in SLAM, this paper is developed from two dimensions. At the solution-oriented level, mainstream methods of filtering dynamic targets in 3D point cloud are introduced in detail, such as the ray-tracing-based approach, the visibility-based approach, the segmentation-based approach, and others. Then, at the problem-oriented level, this paper classifies dynamic objects and summarizes the corresponding processing strategies for different categories in the SLAM framework, such as online real-time filtering, post-processing after the mapping, and Long-term SLAM. Finally, the development trends and research directions of dynamic object filtering in SLAM based on 3D LiDAR are discussed and predicted.

YPL-SLAM: A Simultaneous Localization and Mapping Algorithm for Point-line Fusion in Dynamic Environments.

Simultaneous Localization and Mapping (SLAM) is one of the key technologies with which to address the autonomous navigation of mobile robots, utilizing environmental features to determine a robot's position and create a map of its surroundings. Currently, visual SLAM algorithms typically yield precise and dependable outcomes in static environments, and many algorithms opt to filter out the feature points in dynamic regions. However, when there is an increase in the number of dynamic objects within the camera's view, this approach might result in decreased accuracy or tracking failures. Therefore, this study proposes a solution called YPL-SLAM based on ORB-SLAM2. The solution adds a target recognition and region segmentation module to determine the dynamic region, potential dynamic region, and static region; determines the state of the potential dynamic region using the RANSAC method with polar geometric constraints; and removes the dynamic feature points. It then extracts the line features of the non-dynamic region and finally performs the point-line fusion optimization process using a weighted fusion strategy, considering the image dynamic score and the number of successful feature point-line matches, thus ensuring the system's robustness and accuracy. A large number of experiments have been conducted using the publicly available TUM dataset to compare YPL-SLAM with globally leading SLAM algorithms. The results demonstrate that the new algorithm surpasses ORB-SLAM2 in terms of accuracy (with a maximum improvement of 96.1%) while also exhibiting a significantly enhanced operating speed compared to Dyna-SLAM.

Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception.

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual-inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Comparative Study on Simulated Outdoor Navigation for Agricultural Robots.

This research presents a comprehensive comparative analysis of SLAM algorithms and Deep Neural Network (DNN)-based Behavior Cloning (BC) navigation in outdoor agricultural environments. The study categorizes SLAM algorithms into laser-based and vision-based approaches, addressing the specific challenges posed by uneven terrain and the similarity between aisles in an orchard farm. The DNN-based BC navigation technique proves efficient, exhibiting reduced human intervention and providing a viable alternative for agricultural navigation. Despite the DNN-based BC navigation approach taking more time to reach its target due to a constant throttle limit for steady speed, the overall performance in terms of driving deviation and human intervention is notable compared to conventional SLAM algorithms. We provide comprehensive evaluation criteria for selecting optimal techniques for outdoor agricultural navigations. The algorithms were tested in three different scenarios: Precision, Speed, and Autonomy. Our proposed performance metric, P, is weighted and normalized. The DNN-based BC algorithm showed the best performance among the others, with a performance of 0.92 in the Precision and Autonomy scenarios. When Speed is more important, the RTAB-Map showed the best score with 0.96. In a case where Autonomy has a higher priority, Gmapping also showed a comparable performance of 0.92 with the DNN-based BC.

A Compact Handheld Sensor Package with Sensor Fusion for Comprehensive and Robust 3D Mapping.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

Research on Mobile Robot Navigation Method Based on Semantic Information.

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints.

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite's position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method's potential for practical applications.

Online Scene Semantic Understanding Based on Sparsely Correlated Network for AR.

Real-world understanding serves as a medium that bridges the information world and the physical world, enabling the realization of virtual-real mapping and interaction. However, scene understanding based solely on 2D images faces problems such as a lack of geometric information and limited robustness against occlusion. The depth sensor brings new opportunities, but there are still challenges in fusing depth with geometric and semantic priors. To address these concerns, our method considers the repeatability of video stream data and the sparsity of newly generated data. We introduce a sparsely correlated network architecture (SCN) designed explicitly for online RGBD instance segmentation. Additionally, we leverage the power of object-level RGB-D SLAM systems, thereby transcending the limitations of conventional approaches that solely emphasize geometry or semantics. We establish correlation over time and leverage this correlation to develop rules and generate sparse data. We thoroughly evaluate the system's performance on the NYU Depth V2 and ScanNet V2 datasets, demonstrating that incorporating frame-to-frame correlation leads to significantly improved accuracy and consistency in instance segmentation compared to existing state-of-the-art alternatives. Moreover, using sparse data reduces data complexity while ensuring the real-time requirement of 18 fps. Furthermore, by utilizing prior knowledge of object layout understanding, we showcase a promising application of augmented reality, showcasing its potential and practicality.

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation.

In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community.

The Influence of Micro-Hexapod Walking-Induced Pose Changes on LiDAR-SLAM Mapping Performance.

Micro-hexapods, well-suited for navigating tight or uneven spaces and suitable for mass production, hold promise for exploration by robot groups, particularly in disaster scenarios. However, research on simultaneous localization and mapping (SLAM) for micro-hexapods has been lacking. Previous studies have not adequately addressed the development of SLAM systems considering changes in the body axis, and there is a lack of comparative evaluation with other movement mechanisms. This study aims to assess the influence of walking on SLAM capabilities in hexapod robots. Experiments were conducted using the same SLAM system and LiDAR on both a hexapod robot and crawler robot. The study compares map accuracy and LiDAR point cloud data through pattern matching. The experimental results reveal significant fluctuations in LiDAR point cloud data in hexapod robots due to changes in the body axis, leading to a decrease in map accuracy. In the future, the development of SLAM systems considering body axis changes is expected to be crucial for multi-legged robots like micro-hexapods. Therefore, we propose the implementation of a system that incorporates body axis changes during locomotion using inertial measurement units and similar sensors.

An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View.

Outdoor Location-Based Augmented Reality (LAR) applications require precise positioning for seamless integrations of virtual content into immersive experiences. However, common solutions in outdoor LAR applications rely on traditional smartphone sensor fusion methods, such as the Global Positioning System (GPS) and compasses, which often lack the accuracy needed for precise AR content alignments. In this paper, we introduce an innovative approach to enhance LAR anchor precision in outdoor environments. We leveraged Visual Simultaneous Localization and Mapping (VSLAM) technology, in combination with innovative cloud-based methodologies, and harnessed the extensive visual reference database of Google Street View (GSV), to address the accuracy limitation problems. For the evaluation, 10 Point of Interest (POI) locations were used as anchor point coordinates in the experiments. We compared the accuracies between our approach and the common sensor fusion LAR solution comprehensively involving accuracy benchmarking and running load performance testing. The results demonstrate substantial enhancements in overall positioning accuracies compared to conventional GPS-based approaches for aligning AR anchor content in the real world.