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Outdoor Localization
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ROVER: A Multi-Season Dataset for Visual SLAM
PEnG: Pose-Enhanced Geo-Localisation
SpaGBOL: Spatial-Graph-Based Orientated Localisation
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization
Handling Device Heterogeneity for Deep Learning-based Localization
DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments
Attention-aided Outdoor Localization in Commercial 5G NR Systems
Representing 3D sparse map points and lines for camera relocalization
Improved Indoor Localization with Machine Learning Techniques for IoT applications
LEO Satellite and RIS: Two Keys to Seamless Indoor and Outdoor Localization
BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation
Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps
Simultaneous Indoor and Outdoor 3D Localization with STAR-RIS-Assisted Millimeter Wave Systems
OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization
DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
Do We Really Need Scene-specific Pose Encoders?
RSSI-based Outdoor Localization with Single Unmanned Aerial Vehicle
Hierarchical Scene Coordinate Classification and Regression for Visual Localization
A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
Improved Visual Relocalization by Discovering Anchor Points
CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.