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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 531–540 of 572 papers

TitleStatusHype
ORB-SLAM: a Versatile and Accurate Monocular SLAM SystemCode0
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and MappingCode0
Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAMCode0
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single ImageCode0
Incremental 3D Line Segment Extraction from Semi-dense SLAMCode0
Neural SLAM: Learning to Explore with External MemoryCode0
CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth PredictionCode0
VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and DatasetsCode0
High-Quality, ROS Compatible Video Encoding and Decoding for High-Definition DatasetsCode0
Continuous Direct Sparse Visual Odometry from RGB-D ImagesCode0
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