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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 381–390 of 572 papers

TitleStatusHype
EvenNICER-SLAM: Event-based Neural Implicit Encoding SLAM—0
Exactly Sparse Gaussian Variational Inference with Application to Derivative-Free Batch Nonlinear State Estimation—0
Experience Dependent Formation of Global Coherent Representation of Environment by Grid Cells and Head Direction Cells—0
SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences—0
SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments—0
Space-Time Localization and Mapping—0
SPAQ-DL-SLAM: Towards Optimizing Deep Learning-based SLAM for Resource-Constrained Embedded Platforms—0
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation—0
Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum—0
Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots—0
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