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 331–340 of 572 papers

TitleStatusHype
Data Fusion for Radio Frequency SLAM with Robust Sampling—0
Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning—0
Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Deep Learning for Visual Localization and Mapping: A Survey—0
Deep Learning Reforms Image Matching: A Survey and Outlook—0
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors—0
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction—0
Deep Visual Odometry Methods for Mobile Robots—0
DeepVO: A Deep Learning approach for Monocular Visual Odometry—0
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