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 251–300 of 572 papers

TitleStatusHype
Particle Filter SLAM for Vehicle Localization—0
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments—0
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design—0
PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes—0
PMBM-based SLAM Filters in 5G mmWave Vehicular Networks—0
PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection—0
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments—0
PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time—0
Position Estimation of Camera Based on Unsupervised Learning—0
Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM—0
Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression—0
Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization—0
Privacy-Protection Drone Patrol System based on Face Anonymization—0
Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network—0
Pushing the Envelope of Rotation Averaging for Visual SLAM—0
Quadrotor Control on SU(2) R^3 with SLAM Integration—0
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry—0
Radio SLAM for 6G Systems at THz Frequencies: Design and Experimental Validation—0
Range-Only Bearing Estimator for Localization and Mapping—0
Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions—0
Real-time 3D Reconstruction on Construction Site using Visual SLAM and UAV—0
Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs—0
Real-time Indoor Scene Reconstruction with RGBD and Inertia Input—0
Real-Time Monocular Object-Model Aware Sparse SLAM—0
Real-time Nonrigid Mosaicking of Laparoscopy Images—0
Reconfigurable Holographic Surface Aided Wireless Simultaneous Localization and Mapping—0
Reflex-Based Open-Vocabulary Navigation without Prior Knowledge Using Omnidirectional Camera and Multiple Vision-Language Models—0
Removing Dynamic Objects for Static Scene Reconstruction using Light Fields—0
Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry—0
Revisiting visual-inertial structure from motion for odometry and SLAM initialization—0
RGB-Depth SLAM Review—0
RGB-D Odometry and SLAM—0
RGB-D SLAM in Dynamic Environments Using Point Correlations—0
Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM—0
RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM—0
Robust Large Scale Monocular Visual SLAM—0
Robust Monocular SLAM for Egocentric Videos—0
Robust Snapshot Radio SLAM—0
Rotational Outlier Identification in Pose Graphs Using Dual Decomposition—0
Rotation Averaging: A Primal-Dual Method and Closed-Forms in Cycle Graphs—0
S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization—0
S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM—0
SafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment—0
Scaffold-SLAM: Structured 3D Gaussians for Simultaneous Localization and Photorealistic Mapping—0
Scalable Structure From Motion for Densely Sampled Videos—0
SD-6DoF-ICLK: Sparse and Deep Inverse Compositional Lucas-Kanade Algorithm on SE(3)—0
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance—0
Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots—0
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