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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 201–210 of 572 papers

TitleStatusHype
A Survey on Reinforcement Learning Applications in SLAM—0
Reflex-Based Open-Vocabulary Navigation without Prior Knowledge Using Omnidirectional Camera and Multiple Vision-Language Models—0
CorrAdaptor: Adaptive Local Context Learning for Correspondence PruningCode0
Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods—0
Towards Real-Time Gaussian Splatting: Accelerating 3DGS through Photometric SLAM—0
Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop ClosingCode0
High-Quality, ROS Compatible Video Encoding and Decoding for High-Definition DatasetsCode0
NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding—0
Solving Short-Term Relocalization Problems In Monocular Keyframe Visual SLAM Using Spatial And Semantic DataCode0
3D Reconstruction of the Human Colon from Capsule Endoscope Video—0
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