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 161–170 of 572 papers

TitleStatusHype
LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and MappingCode0
ecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph OptimizationCode0
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environmentCode0
Kernel learning for visual perceptionCode0
Keyfilter-Aware Real-Time UAV Object TrackingCode0
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop ClosureCode0
Beyond Photometric Loss for Self-Supervised Ego-Motion EstimationCode0
BEyond observation: an approach for ObjectNavCode0
Incremental 3D Line Segment Extraction from Semi-dense SLAMCode0
InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in Indoor Pedestrian-Rich Spaces for Human NavigationCode0
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