most citedRobust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20241 cited

Event-based Sensor Fusion and Application on Odometry: A Survey

Jiaqiang Zhang, Xianjia Yu, Ha Sier +2

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness, offering notable advantages in environments characterized by high-speed mo…

cs.LG2024

Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality

Haizhou Zhang, Xianjia Yu, Tomi Westerlund

Federated learning (FL) has become one of the key methods for privacy-preserving collaborative learning, as it enables the transfer of models without requiring local data exchange.…

cs.RO2023

Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset

Iacopo Catalano, Xianjia Yu, Jorge Pena Queralta

With the increasing prevalence of drones in various industries, the navigation and tracking of unmanned aerial vehicles (UAVs) in challenging environments, particularly GNSS-denied…

cs.RO2023

LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation

Haizhou Zhang, Xianjia Yu, Sier Ha +1

Keypoint detection and description play a pivotal role in various robotics and autonomous applications including visual odometry (VO), visual navigation, and Simultaneous localizat…

cs.RO2023

Benchmarking UWB-Based Infrastructure-Free Positioning and Multi-Robot Relative Localization: Dataset and Characterization

Paola Torrico Morón, Sahar Salimpour, Lei Fu +3

Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial I…

cs.RO20232 cited

Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

Li Qingqing, Yu Xianjia, Jorge Peña Queralta +1

Integrating multiple LiDAR sensors can significantly enhance a robot's perception of the environment, enabling it to capture adequate measurements for simultaneous localization and…