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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 61–70 of 572 papers

TitleStatusHype
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and MappingCode1
UncLe-SLAM: Uncertainty Learning for Dense Neural SLAMCode1
iSLAM: Imperative SLAMCode1
Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAMCode1
Event-based Simultaneous Localization and Mapping: A Comprehensive SurveyCode1
Learning How To Robustly Estimate Camera Pose in Endoscopic VideosCode1
Loop Closure Detection Based on Object-level Spatial Layout and Semantic ConsistencyCode1
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous DrivingCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
Real-Time Simultaneous Localization and Mapping with LiDAR intensityCode1
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