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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 91–100 of 572 papers

TitleStatusHype
P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose UncertaintyCode0
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning—0
Object Depth and Size Estimation using Stereo-vision and Integration with SLAM—0
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data—0
Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric—0
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
RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions TransformCode2
LoopSplat: Loop Closure by Registering 3D Gaussian SplatsCode3
CorrAdaptor: Adaptive Local Context Learning for Correspondence PruningCode0
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