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
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Evaluation of the Robustness of Visual SLAM Methods in Different Environments—0
Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms—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
Closing the Calibration Loop: An Inside-out-tracking Paradigm for Augmented Reality in Orthopedic Surgery—0
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers—0
ADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting—0
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