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 71–80 of 572 papers

TitleStatusHype
AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty MinimizationCode1
HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D ImagesCode1
S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAMCode1
LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile AgentsCode1
General Place Recognition Survey: Towards the Real-world Autonomy AgeCode1
D^3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO GuidanceCode1
Object Structural Points Representation for Graph-based Semantic Monocular Localization and MappingCode1
ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and MappingCode1
EndoMapper dataset of complete calibrated endoscopy proceduresCode1
Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAMCode1
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