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 121–130 of 572 papers

TitleStatusHype
Splat-SLAM: Globally Optimized RGB-only SLAM with 3D GaussiansCode2
NeB-SLAM: Neural Blocks-based Salable RGB-D SLAM for Unknown Scenes—0
CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World EnvironmentsCode1
Monocular Gaussian SLAM with Language Extended Loop Closure—0
Outlier-Robust Long-Term Robotic Mapping Leveraging Ground Segmentation—0
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPUCode3
MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth Smooth Regularization—0
General Place Recognition Survey: Towards Real-World AutonomyCode2
Multipath-based SLAM with Cooperation and Map Fusion in MIMO Systems—0
S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM—0
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