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
A Geometric Nonlinear Stochastic Filter for Simultaneous Localization and Mapping—0
Data Fusion for Radio Frequency SLAM with Robust Sampling—0
Data Fusion for Multipath-Based SLAM: Combining Information from Multiple Propagation Paths—0
RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network—0
A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior—0
A Belief Propagation Algorithm for Multipath-based SLAM with Multiple Map Features: A mmWave MIMO Application—0
DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments—0
cuVSLAM: CUDA accelerated visual odometry and mapping—0
CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy—0
A Fast Feature Point Matching Algorithm Based on IMU Sensor—0
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