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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 161–170 of 572 papers

TitleStatusHype
DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems—0
vS-Graphs: Integrating Visual SLAM and Situational Graphs through Multi-level Scene Understanding—0
ecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph OptimizationCode0
FGS-SLAM: Fourier-based Gaussian Splatting for Real-time SLAM with Sparse and Dense Map Fusion—0
From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations—0
Enhancing Feature Tracking Reliability for Visual Navigation using Real-Time Safety Filter—0
SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios—0
Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures—0
Self-Organizing Edge Computing Distribution Framework for Visual SLAM—0
GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping—0
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