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 221–230 of 572 papers

TitleStatusHype
Hierarchical Segment-based Optimization for SLAM—0
GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping—0
A Two-stage Unsupervised Approach for Low light Image Enhancement—0
A Geometric Nonlinear Stochastic Filter for Simultaneous Localization and Mapping—0
HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields—0
Homography Estimation with Convolutional Neural Networks Under Conditions of Variance—0
How far should self-driving cars see? Effect of observation range on vehicle self-localization—0
GRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone—0
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers—0
GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization—0
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