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 391–400 of 572 papers

TitleStatusHype
A comparative evaluation of learned feature descriptors on hybrid monocular visual SLAM methods—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
Map completion from partial observation using the global structure of multiple environmental maps—0
GRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone—0
Hippocampal formation-inspired probabilistic generative model—0
Multiview Sensing With Unknown Permutations: An Optimal Transport Approach—0
Real-time Nonrigid Mosaicking of Laparoscopy Images—0
Advances in Inference and Representation for Simultaneous Localization and Mapping—0
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