Papers Simultaneous Localization and Mapping
“Simultaneous Localization and Mapping” 태그가 달린 논문 572편 · 필터 해제
FGS-SLAM: Fourier-based Gaussian Splatting for Real-time SLAM with Sparse and Dense Map Fusion
3D gaussian splatting has advanced simultaneous localization and mapping (SLAM) technology by enabling real-time positioning and the construction of high-fidelity maps. However, the uncertainty in gaussian position and i…
Simultaneous Localization and MappingvS-Graphs: Integrating Visual SLAM and Situational Graphs through Multi-level Scene Understanding
Current Visual Simultaneous Localization and Mapping (VSLAM) systems often struggle to create maps that are both semantically rich and easily interpretable. While incorporating semantic scene knowledge aids in building r…
Scene UnderstandingSimultaneous Localization and Mappingecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph Optimization
Factor graph optimization serves as a fundamental framework for robotic perception, enabling applications such as pose estimation, simultaneous localization and mapping (SLAM), structure-from-motion (SfM), and situationa…
Autonomous VehiclesPose EstimationSimultaneous Localization and MappingFrom Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations
In computer vision, the development of robust algorithms capable of generalizing effectively in real-world scenarios more and more often requires large-scale datasets collected under diverse environmental conditions. How…
Simultaneous Localization and MappingSynthetic Data GenerationVisual Place RecognitionEnhancing Feature Tracking Reliability for Visual Navigation using Real-Time Safety Filter
Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailable. In many visual navigation systems, lo…
Pose EstimationSimultaneous Localization and MappingVisual NavigationFlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An important field of research is the generation o…
Autonomous Drivingmotion predictionSimultaneous Localization and MappingAdvancing Dense Endoscopic Reconstruction with Gaussian Splatting-driven Surface Normal-aware Tracking and Mapping
Simultaneous Localization and Mapping (SLAM) is essential for precise surgical interventions and robotic tasks in minimally invasive procedures. While recent advancements in 3D Gaussian Splatting (3DGS) have improved SLA…
3DGSNovel View SynthesisSimultaneous Localization and MappingSurface ReconstructionSSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios
Vehicle detection and localization in complex traffic scenarios pose significant challenges due to the interference of moving objects. Traditional methods often rely on outlier exclusions or semantic segmentations, which…
Autonomous NavigationComputational EfficiencyMoving Object Detectionobject-detection+3GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping
In recent years, 3D Gaussian splatting (3D-GS) has emerged as a novel scene representation approach. However, existing vision-only 3D-GS methods often rely on hand-crafted heuristics for point-cloud densification and fac…
GPUSensor FusionSimultaneous Localization and MappingSelf-Organizing Edge Computing Distribution Framework for Visual SLAM
Localization within a known environment is a crucial capability for mobile robots. Simultaneous Localization and Mapping (SLAM) is a prominent solution to this problem. SLAM is a framework that consists of a diverse set …
Edge-computingSimultaneous Localization and MappingUnified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures
Visual-Spatial Systems has become increasingly essential in concrete crack inspection. However, existing methods often lacks adaptability to diverse scenarios, exhibits limited robustness in image-based approaches, and s…
3D ReconstructionCrack SegmentationSimultaneous Localization and MappingSP-SLAM: Neural Real-Time Dense SLAM With Scene Priors
Neural implicit representations have recently shown promising progress in dense Simultaneous Localization And Mapping (SLAM). However, existing works have shortcomings in terms of reconstruction quality and real-time per…
Simultaneous Localization and MappingSurface ReconstructionScaffold-SLAM: Structured 3D Gaussians for Simultaneous Localization and Photorealistic Mapping
3D Gaussian Splatting (3DGS) has recently revolutionized novel view synthesis in the Simultaneous Localization and Mapping (SLAM). However, existing SLAM methods utilizing 3DGS have failed to provide high-quality novel v…
3DGSNovel View SynthesisSimultaneous Localization and MappingPanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
Understanding geometric, semantic, and instance information in 3D scenes from sequential video data is essential for applications in robotics and augmented reality. However, existing Simultaneous Localization and Mapping…
3D Instance Segmentation3D Reconstruction3D Scene Reconstruction3D Semantic Segmentation+5Query Quantized Neural SLAM
Neural implicit representations have shown remarkable abilities in jointly modeling geometry, color, and camera poses in simultaneous localization and mapping (SLAM). Current methods use coordinates, positional encodings…
Simultaneous Localization and MappingEnergy-Efficient SLAM via Joint Design of Sensing, Communication, and Exploration Speed
To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots perfor…
Simultaneous Localization and MappingDynamic semantic VSLAM with known and unknown objects
Traditional Visual Simultaneous Localization and Mapping (VSLAM) systems assume a static environment, which makes them ineffective in highly dynamic settings. To overcome this, many approaches integrate semantic informat…
Optical Flow EstimationSimultaneous Localization and MappingNFL-BA: Improving Endoscopic SLAM with Near-Field Light Bundle Adjustment
Simultaneous Localization And Mapping (SLAM) from a monocular endoscopy video can enable autonomous navigation, guidance to unsurveyed regions, and 3D visualizations, which can significantly improve endoscopy experience …
3DGSAutonomous NavigationSimultaneous Localization and MappingDyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera
We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocu…
Simultaneous Localization and MappingStatic-Dynamic Class-level Perception Consistency in Video Semantic Segmentation
Video semantic segmentation(VSS) has been widely employed in lots of fields, such as simultaneous localization and mapping, autonomous driving and surveillance. Its core challenge is how to leverage temporal information …
Autonomous DrivingContrastive LearningOptical Flow EstimationSemantic Segmentation+2