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 211–220 of 572 papers

TitleStatusHype
A Hybrid Learner for Simultaneous Localization and Mapping—0
A Belief Propagation Approach for Direct Multipath-Based SLAM—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera—0
Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature—0
Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery—0
Haris: an Advanced Autonomous Mobile Robot for Smart Parking Assistance—0
Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning—0
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting—0
GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping—0
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