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 191–200 of 572 papers

TitleStatusHype
Initialization of Monocular Visual Navigation for Autonomous Agents Using Modified Structure from Small Motion—0
Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research—0
Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization—0
SPAQ-DL-SLAM: Towards Optimizing Deep Learning-based SLAM for Resource-Constrained Embedded Platforms—0
Bundle Adjustment in the Eager Mode—0
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning—0
P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose UncertaintyCode0
Object Depth and Size Estimation using Stereo-vision and Integration with SLAM—0
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data—0
Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric—0
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