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 471–480 of 572 papers

TitleStatusHype
Active collaboration in relative observation for Multi-agent visual SLAM based on Deep Q Network—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR DescriptorsCode0
SE-SLAM: Semi-Dense Structured Edge-Based Monocular SLAM—0
How far should self-driving cars see? Effect of observation range on vehicle self-localization—0
Hybrid Camera Pose Estimation with Online Partitioning for SLAM—0
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM—0
Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAMCode0
Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots—0
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environmentCode0
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