Xianjia Yu

Orcid: 0000-0002-9042-3730

According to our database1, Xianjia Yu authored at least 24 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Enhancing the Reliability of LiDAR Point Cloud Sampling: A Colorization and Super-Resolution Approach Based on LiDAR-Generated Images.
CoRR, 2024

2023
LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation.
Remote. Sens., October, 2023

A Benchmark for Multi-Modal LiDAR SLAM with Ground Truth in GNSS-Denied Environments.
Remote. Sens., July, 2023

General-Purpose Deep Learning Detection and Segmentation Models for Images from a Lidar-Based Camera Sensor.
Sensors, March, 2023

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization.
Frontiers Robotics AI, 2023

Comparison of DDS, MQTT, and Zenoh in Edge-to-Edge and Edge-to-Cloud Communication for Distributed ROS 2 Systems.
CoRR, 2023

Loosely Coupled Odometry, UWB Ranging, and Cooperative Spatial Detection for Relative Monte-Carlo Multi-Robot Localization.
CoRR, 2023

Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments.
CoRR, 2023

Benchmarking UWB-Based Infrastructure-Free Positioning and Multi-Robot Relative Localization: Dataset and Characterization.
Proceedings of the IEEE Sensors Applications Symposium, 2023

Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset.
Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2023

UAV Tracking with Lidar as a Camera Sensor in GNSS-Denied Environments.
Proceedings of the International Conference on Localization and GNSS, 2023

UAV Tracking with Solid-State Lidars: Dynamic Multi-Frequency Scan Integration.
Proceedings of the 21st International Conference on Advanced Robotics, 2023

2022
Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor.
CoRR, 2022

Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness.
Proceedings of the Seventh International Conference on Fog and Mobile Edge Computing, 2022

Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things.
Proceedings of the Seventh International Conference on Fog and Mobile Edge Computing, 2022

Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer.
Proceedings of the 13th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2022) / The 12th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2022), 2022

2021
An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems.
CoRR, 2021

Towards Large-Scale Scalable MAV Swarms with ROS2 and UWB-based Situated Communication.
CoRR, 2021

Cooperative UWB-Based Localization for Outdoors Positioning and Navigation of UAVs aided by Ground Robots.
CoRR, 2021

Applications of UWB Networks and Positioning to Autonomous Robots and Industrial Systems.
Proceedings of the 10th Mediterranean Conference on Embedded Computing, 2021

Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds.
Proceedings of the 20th International Conference on Advanced Robotics, 2021

Federated Learning in Robotic and Autonomous Systems.
Proceedings of the 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2021) / The 16th International Conference on Future Networks and Communications (FNC-2021) / The 11th International Conference on Sustainable Energy Information Technology, 2021

2019
Classification of multichannel surface-electromyography signals based on convolutional neural networks.
J. Ind. Inf. Integr., 2019


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