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
Region Prediction for Efficient Robot Localization on Large MapsCode0
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous DrivingCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
Task Space Control of Robot Manipulators based on Visual SLAM—0
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM—0
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera—0
Real-Time Simultaneous Localization and Mapping with LiDAR intensityCode1
Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories—0
Dense RGB SLAM with Neural Implicit Maps—0
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information—0
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