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 231–240 of 572 papers

TitleStatusHype
Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology—0
LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping—0
Language, Environment, and Robotic Navigation—0
MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements—0
EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting—0
DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding—0
A Belief Propagation Algorithm for Multipath-based SLAM with Multiple Map Features: A mmWave MIMO Application—0
Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning—0
Loopy-SLAM: Dense Neural SLAM with Loop Closures—0
Particle Filter SLAM for Vehicle Localization—0
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