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 551–572 of 572 papers

TitleStatusHype
Indoor simultaneous localization and mapping based on fringe projection profilometry—0
Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field—0
Indoor Smartphone SLAM with Learned Echoic Location Features—0
Inertial Guided Uncertainty Estimation of Feature Correspondence in Visual-Inertial Odometry/SLAM—0
Initialization of Monocular Visual Navigation for Autonomous Agents Using Modified Structure from Small Motion—0
InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes—0
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset—0
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM—0
Iterated Posterior Linearization PMB Filter for 5G SLAM—0
IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping—0
Jointly Optimized Global-Local Visual Localization of UAVs—0
Joint Range and Angle Estimation for FMCW MIMO Radar and Its Application—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
Keyframe-Based Visual-Inertial Online SLAM with Relocalization—0
Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping—0
Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias—0
Language, Environment, and Robotic Navigation—0
Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding—0
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping—0
Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching—0
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction—0
Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors—0
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