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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 191–200 of 572 papers

TitleStatusHype
DVI-SLAM: A Dual Visual Inertial SLAM Network—0
DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance FieldsCode1
Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression—0
GO-SLAM: Global Optimization for Consistent 3D Instant ReconstructionCode2
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and MappingCode1
Deep Learning for Visual Localization and Mapping: A Survey—0
VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition—0
SLAM for Multiple Extended Targets using 5G Signal—0
Stereo Visual Odometry with Deep Learning-Based Point and Line Feature Matching using an Attention Graph Neural Network—0
Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents—0
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