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Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is the task of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.

( Image credit: ORB-SLAM2 )

Papers

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Showing 521–530 of 572 papers

TitleStatusHype
Self-Improving SLAM in Dynamic Environments: Learning When to MaskCode0
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple ObjectsCode0
CorrAdaptor: Adaptive Local Context Learning for Correspondence PruningCode0
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop ClosureCode0
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM AugmentationCode0
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth PredictionCode0
UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar MarkersCode0
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAMCode0
InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in Indoor Pedestrian-Rich Spaces for Human NavigationCode0
P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose UncertaintyCode0
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