Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
Simultaneous Localization and Mapping Using Active mmWave Sensing in 5G NR
Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced e…
Loop Closure DetectionPoint Cloud GenerationPoint Cloud RegistrationSimultaneous Localization and MappingEndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting
Efficient three-dimensional reconstruction and real-time visualization are critical in surgical scenarios such as endoscopy. In recent years, 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in effici…
3DGS3D ReconstructionNovel View SynthesisOptical Flow Estimation+2Adaptive Multipath-Based SLAM for Distributed MIMO Systems
Localizing users and mapping the environment using radio signals is a key task in emerging applications such as reliable communications, location-aware security, and safety critical navigation. Recently introduced multip…
Simultaneous Localization and MappingPosterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. …
PositionSimultaneous Localization and MappingSuperPoint-SLAM3: Augmenting ORB-SLAM3 with Deep Features, Adaptive NMS, and Learning-Based Loop Closure
Visual simultaneous localization and mapping (SLAM) must remain accurate under extreme viewpoint, scale and illumination variations. The widely adopted ORB-SLAM3 falters in these regimes because it relies on hand-crafted…
Simultaneous Localization and MappingA Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
In the field of multi-sensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMUs are widely used to build simple and effective visual-inertial systems. However, limited research has explor…
Deep Reinforcement LearningSensor FusionSimultaneous Localization and MappingState EstimationLRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantage…
Autonomous DrivingMixed RealitySimultaneous Localization and MappingTensor DecompositionFaster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs
The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual reality, which primarily focuses on detecting feature points from visual im…
GPUSimultaneous Localization and MappingLoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
In this study, we introduce LoopDB, which is a challenging loop closure dataset comprising over 1000 images captured across diverse environments, including parks, indoor scenes, parking spaces, as well as centered around…
BenchmarkingSimultaneous Localization and MappingDy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments
Current Simultaneous Localization and Mapping (SLAM) methods based on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting excel in reconstructing static 3D scenes but struggle with tracking and reconstruction in dynam…
3DGSNeRFOptical Flow EstimationPose Estimation+1Deep Learning Reforms Image Matching: A Survey and Outlook
Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in computer vision and underpins a wide range of applications, including visu…
3D ReconstructionDeep LearningHomography EstimationSimultaneous Localization and Mapping+2SupeRANSAC: One RANSAC to Rule Them All
Robust estimation is a cornerstone in computer vision, particularly for tasks like Structure-from-Motion and Simultaneous Localization and Mapping. RANSAC and its variants are the gold standard for estimating geometric m…
AllPose EstimationSimultaneous Localization and MappingcuVSLAM: CUDA accelerated visual odometry and mapping
Accurate and robust pose estimation is a key requirement for any autonomous robot. We present cuVSLAM, a state-of-the-art solution for visual simultaneous localization and mapping, which can operate with a variety of vis…
Edge-computingPose EstimationSimultaneous Localization and MappingVisual OdometryVTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians
Jointly estimating camera poses and mapping scenes from RGBD images is a fundamental task in simultaneous localization and mapping (SLAM). State-of-the-art methods employ 3D Gaussians to represent a scene, and render the…
GPUSimultaneous Localization and MappingLEG-SLAM: Real-Time Language-Enhanced Gaussian Splatting for SLAM
Modern Gaussian Splatting methods have proven highly effective for real-time photorealistic rendering of 3D scenes. However, integrating semantic information into this representation remains a significant challenge, espe…
Simultaneous Localization and MappingBlack-box Adversarial Attacks on CNN-based SLAM Algorithms
Continuous advancements in deep learning have led to significant progress in feature detection, resulting in enhanced accuracy in tasks like Simultaneous Localization and Mapping (SLAM). Nevertheless, the vulnerability o…
Simultaneous Localization and MappingUP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments
Recent 3D Gaussian Splatting (3DGS) techniques for Visual Simultaneous Localization and Mapping (SLAM) have significantly progressed in tracking and high-fidelity mapping. However, their sequential optimization framework…
3DGSSimultaneous Localization and MappingVisual Loop Closure Detection Through Deep Graph Consensus
Visual loop closure detection traditionally relies on place recognition methods to retrieve candidate loops that are validated using computationally expensive RANSAC-based geometric verification. As false positive loop c…
Computational EfficiencyGraph Neural NetworkLoop Closure DetectionSimultaneous Localization and MappingADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting
Recent advancements in Neural Radiance Fields (NeRF) and 3D Gaussian-based Simultaneous Localization and Mapping (SLAM) methods have demonstrated exceptional localization precision and remarkable dense mapping performanc…
NeRFobject-detectionObject DetectionObject Recognition+2Place Recognition: A Comprehensive Review, Current Challenges and Future Directions
Place recognition is a cornerstone of vehicle navigation and mapping, which is pivotal in enabling systems to determine whether a location has been previously visited. This capability is critical for tasks such as loop c…
3D Place RecognitionCross-modal place recognitionSimultaneous Localization and MappingVisual Place Recognition