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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 351–360 of 572 papers

TitleStatusHype
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
Graph-based Thermal-Inertial SLAM with Probabilistic Neural NetworksCode1
A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior—0
A comparative evaluation of learned feature descriptors on hybrid monocular visual SLAM methods—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
LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method—0
SD-6DoF-ICLK: Sparse and Deep Inverse Compositional Lucas-Kanade Algorithm on SE(3)—0
Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models—0
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