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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 281–290 of 572 papers

TitleStatusHype
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM—0
An Expeditious Spatial Mean Radiant Temperature Mapping Framework using Visual SLAM and Semantic Segmentation—0
An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm—0
An Online Semantic Mapping System for Extending and Enhancing Visual SLAM—0
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots—0
A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis—0
A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition—0
A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method—0
Appearance-based indoor localization: A comparison of patch descriptor performance—0
AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization—0
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