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
Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots—0
Spatiotemporal Articulated Models for Dynamic SLAM—0
Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization—0
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM—0
SplaTAM: Splat Track & Map 3D Gaussians for Dense RGB-D SLAM—0
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM—0
SP-SLAM: Neural Real-Time Dense SLAM With Scene Priors—0
SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios—0
STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM—0
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—0
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