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 131–140 of 572 papers

TitleStatusHype
A Collaborative Visual SLAM Framework for Service Robots—0
Dense RGB SLAM with Neural Implicit Maps—0
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM—0
Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images—0
Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations—0
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization—0
Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach—0
Degenerate Motions in Multicamera Cluster SLAM with Non-overlapping Fields of View—0
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM—0
Autonomous Asteroid Characterization Through Nanosatellite Swarming—0
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