Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods
Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization an…
3D Scene Reconstructionglobal-optimizationNeRFSimultaneous Localization and MappingTowards Real-Time Gaussian Splatting: Accelerating 3DGS through Photometric SLAM
Initial applications of 3D Gaussian Splatting (3DGS) in Visual Simultaneous Localization and Mapping (VSLAM) demonstrate the generation of high-quality volumetric reconstructions from monocular video streams. However, de…
3DGSSimultaneous Localization and MappingBodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications
Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization and Mapping (MVSLAM) has emerged as a p…
3D ReconstructionDepth EstimationMonocular Depth EstimationPose Estimation+1Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing
Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. These environme…
Autonomous NavigationBenchmarkingSimultaneous Localization and MappingHigh-Quality, ROS Compatible Video Encoding and Decoding for High-Definition Datasets
Robotic datasets are important for scientific benchmarking and developing algorithms, for example for Simultaneous Localization and Mapping (SLAM). Modern robotic datasets feature video data of high resolution and high f…
BenchmarkingSimultaneous Localization and MappingNIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding
In recent years, the paradigm of neural implicit representations has gained substantial attention in the field of Simultaneous Localization and Mapping (SLAM). However, a notable gap exists in the existing approaches whe…
Scene UnderstandingSimultaneous Localization and MappingSurface ReconstructionSolving Short-Term Relocalization Problems In Monocular Keyframe Visual SLAM Using Spatial And Semantic Data
In Monocular Keyframe Visual Simultaneous Localization and Mapping (MKVSLAM) frameworks, when incremental position tracking fails, global pose has to be recovered in a short-time window, also known as short-term relocali…
Indoor LocalizationSimultaneous Localization and Mapping3D Reconstruction of the Human Colon from Capsule Endoscope Video
As the number of people affected by diseases in the gastrointestinal system is ever-increasing, a higher demand on preventive screening is inevitable. This will significantly increase the workload on gastroenterologists.…
3D ReconstructionSimultaneous Localization and MappingSurface ReconstructionLearn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM
Simultaneous localization and mapping (SLAM) with implicit neural representations has received extensive attention due to the expressive representation power and the innovative paradigm of continual learning. However, de…
Continual LearningSimultaneous Localization and MappingGV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection
Visual loop closure detection is an important module in visual simultaneous localization and mapping (SLAM), which associates current camera observation with previously visited places. Loop closures correct drifts in tra…
BenchmarkingLoop Closure DetectionPose EstimationSimultaneous Localization and Mapping+1FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration
Simultaneous Localization and Mapping (SLAM) technology enables the construction of environmental maps and localization, serving as a key technique for indoor autonomous navigation of mobile robots. Traditional SLAM meth…
Autonomous NavigationSimultaneous Localization and MappingBatch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned on a DA. In this paper, we propose a no…
Simultaneous Localization and MappingHyperion - A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM
Continuous-Time Simultaneous Localization And Mapping (CTSLAM) has become a promising approach for fusing asynchronous and multi-modal sensor suites. Unlike discrete-time SLAM, which estimates poses discretely, CTSLAM us…
Simultaneous Localization and MappingA Neurosymbolic Approach to Adaptive Feature Extraction in SLAM
Autonomous robots, autonomous vehicles, and humans wearing mixed-reality headsets require accurate and reliable tracking services for safety-critical applications in dynamically changing real-world environments. However,…
Autonomous VehiclesMixed RealityProgram SynthesisSimultaneous Localization and MappingHumans as Checkerboards: Calibrating Camera Motion Scale for World-Coordinate Human Mesh Recovery
Accurate camera motion estimation is essential for recovering global human motion in world coordinates from RGB video inputs. SLAM is widely used for estimating camera trajectory and point cloud, but monocular SLAM does …
Human Mesh RecoveryMotion EstimationSimultaneous Localization and MappingFrom Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking
Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to embodied agent autonomy. While real-worl…
BenchmarkingNeRFSimultaneous Localization and MappingMulticam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
This paper presents a novel approach to visual simultaneous localization and mapping (SLAM) using multiple RGB-D cameras. The proposed method, Multicam-SLAM, significantly enhances the robustness and accuracy of SLAM sys…
Pose EstimationSimultaneous Localization and MappingVisual LocalizationOpen Problem: Active Representation Learning
In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from …
Representation Learningscientific discoverySimultaneous Localization and MappingRotation Averaging: A Primal-Dual Method and Closed-Forms in Cycle Graphs
A cornerstone of geometric reconstruction, rotation averaging seeks the set of absolute rotations that optimally explains a set of measured relative orientations between them. In addition to being an integral part of bun…
Simultaneous Localization and MappingCudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy
Monocular visual simultaneous localization and mapping (V-SLAM) is nowadays an irreplaceable tool in mobile robotics and augmented reality, where it performs robustly. However, human colonoscopies pose formidable challen…
GPUSimultaneous Localization and Mapping