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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 11–20 of 572 papers

TitleStatusHype
Deep Learning Reforms Image Matching: A Survey and Outlook—0
SupeRANSAC: One RANSAC to Rule Them AllCode3
cuVSLAM: CUDA accelerated visual odometry and mapping—0
LEG-SLAM: Real-Time Language-Enhanced Gaussian Splatting for SLAM—0
VTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians—0
Black-box Adversarial Attacks on CNN-based SLAM Algorithms—0
UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments—0
Visual Loop Closure Detection Through Deep Graph Consensus—0
ADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting—0
Place Recognition: A Comprehensive Review, Current Challenges and Future DirectionsCode2
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