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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 411–420 of 572 papers

TitleStatusHype
Semantic SLAM with Autonomous Object-Level Data Association—0
Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias—0
BirdSLAM: Monocular Multibody SLAM in Bird's-Eye View—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping—0
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association—0
Compositional Scalable Object SLAM—0
Pushing the Envelope of Rotation Averaging for Visual SLAM—0
The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation—0
A Two-stage Unsupervised Approach for Low light Image Enhancement—0
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