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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 111–120 of 572 papers

TitleStatusHype
DSP-SLAM: Object Oriented SLAM with Deep Shape PriorsCode1
DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance FieldsCode1
Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic CalibrationCode1
ROVER: A Multi-Season Dataset for Visual SLAMCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
SemanticSLAM: Learning based Semantic Map Construction and Robust Camera LocalizationCode1
D^3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO GuidanceCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D ImagesCode1
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