SOTAVerified

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 281–290 of 572 papers

TitleStatusHype
Object Structural Points Representation for Graph-based Semantic Monocular Localization and MappingCode1
Data Fusion for Radio Frequency SLAM with Robust Sampling—0
ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy—0
ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and MappingCode1
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding—0
S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization—0
PMBM-based SLAM Filters in 5G mmWave Vehicular Networks—0
EndoMapper dataset of complete calibrated endoscopy proceduresCode1
Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAMCode1
Indoor simultaneous localization and mapping based on fringe projection profilometry—0
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