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 381–390 of 572 papers

TitleStatusHype
Embedded Vision for Self-Driving on Forest Roads—0
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation—0
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping—0
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition—0
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames—0
Fisheye Lens Camera based Autonomous Valet Parking System—0
Self-optimizing loop sifting and majorization for 3D reconstruction—0
Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs—0
A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior—0
A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis—0
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