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 541–550 of 572 papers

TitleStatusHype
Homography Estimation with Convolutional Neural Networks Under Conditions of Variance—0
How far should self-driving cars see? Effect of observation range on vehicle self-localization—0
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers—0
Hybrid Camera Pose Estimation with Online Partitioning for SLAM—0
Hyperspectral 3D Mapping of Underwater Environments—0
ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy—0
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames—0
Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories—0
Improving Sonar Image Patch Matching via Deep Learning—0
Inclusive STEAM Education: A Framework for Teaching Cod-2 ing and Robotics to Students with Visually Impairment Using 3 Advanced Computer Vision—0
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