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 401–410 of 572 papers

TitleStatusHype
Panoramic annular SLAM with loop closure and global optimization—0
A Geometric Nonlinear Stochastic Filter for Simultaneous Localization and Mapping—0
DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments—0
Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching—0
A Collaborative Visual SLAM Framework for Service Robots—0
Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach—0
Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot—0
A Hybrid Learner for Simultaneous Localization and Mapping—0
CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80—0
RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM—0
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