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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 101–110 of 572 papers

TitleStatusHype
Attention-SLAM: A Visual Monocular SLAM Learning from Human GazeCode1
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and MappingCode1
Learning How To Robustly Estimate Camera Pose in Endoscopic VideosCode1
LGU-SLAM: Learnable Gaussian Uncertainty Matching with Deformable Correlation Sampling for Deep Visual SLAMCode1
Object Structural Points Representation for Graph-based Semantic Monocular Localization and MappingCode1
Deep Depth Estimation from Visual-Inertial SLAMCode1
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure DetectionCode1
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place RecognitionCode1
Loop Closure Detection Based on Object-level Spatial Layout and Semantic ConsistencyCode1
D^3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO GuidanceCode1
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