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 531–540 of 572 papers

TitleStatusHype
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction—0
Backtracking Regression Forests for Accurate Camera RelocalizationCode0
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design—0
Space-Time Localization and Mapping—0
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single ImageCode0
Direction-Aware Semi-Dense SLAM—0
Improving Sonar Image Patch Matching via Deep Learning—0
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM AugmentationCode0
Incremental 3D Line Segment Extraction from Semi-dense SLAMCode0
A Solution for Crime Scene Reconstruction using Time-of-Flight Cameras—0
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