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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 101–110 of 572 papers

TitleStatusHype
Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods—0
Towards Real-Time Gaussian Splatting: Accelerating 3DGS through Photometric SLAM—0
BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical ApplicationsCode1
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
Learn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM—0
Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization—0
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