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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 21–30 of 572 papers

TitleStatusHype
SafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment—0
EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy—0
DERD-Net: Learning Depth from Event-based Ray DensitiesCode0
SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAMCode0
PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection—0
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAMCode1
VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and DatasetsCode0
Nonlinear Observer Design for Landmark-Inertial Simultaneous Localization and Mapping—0
Multimodal Fusion and Vision-Language Models: A Survey for Robot VisionCode1
MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM—0
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