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
Learn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM—0
FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration—0
Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization—0
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM—0
Humans as Checkerboards: Calibrating Camera Motion Scale for World-Coordinate Human Mesh Recovery—0
Open Problem: Active Representation Learning—0
Rotation Averaging: A Primal-Dual Method and Closed-Forms in Cycle Graphs—0
CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy—0
NeB-SLAM: Neural Blocks-based Salable RGB-D SLAM for Unknown Scenes—0
Monocular Gaussian SLAM with Language Extended Loop Closure—0
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