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 101–110 of 572 papers

TitleStatusHype
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Visual place recognition: A survey from deep learning perspectiveCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic CalibrationCode1
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale EnvironmentCode1
Fast and Incremental Loop Closure Detection with Deep Features and Proximity GraphsCode1
Attention-SLAM: A Visual Monocular SLAM Learning from Human GazeCode1
DXSLAM: A Robust and Efficient Visual SLAM System with Deep FeaturesCode1
Deep Depth Estimation from Visual-Inertial SLAMCode1
Deep Keypoint-Based Camera Pose Estimation with Geometric ConstraintsCode1
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