Xinyuan Qiao

University of Toronto

Papers

2

Total Citations

41

H-Index

2

About

Xinyuan Qiao is a leading researcher in indoor localization, with a primary focus on ultra-wideband (UWB) time-difference-of-arrival (TDOA) systems. Their most significant contribution is the creation of the UTIL dataset—the first comprehensive public benchmark for UWB TDOA-based positioning, which has become an essential resource for the field. This work, published in 2024 and cited 34 times, addresses a critical gap by providing standardized data for studying low-cost, scalable indoor localization solutions, particularly for multi-robot applications. Qiao’s research has directly enabled reproducible comparisons and accelerated advancements in this area. Their earlier 2022 version of the dataset, with 7 citations, laid the groundwork for this impact. By democratizing access to high-quality UWB TDOA data, Qiao has empowered researchers and engineers to develop more robust and efficient indoor navigation systems. Their work stands out for its practical relevance, bridging the gap between theoretical localization algorithms and real-world deployment in robotics and IoT.

Research Focus

Key Achievements

2
H-Index
2
Papers
41
Total Citations
21
Avg Citations/Paper
🏆 Most Cited Paper
UTIL: An ultra-wideband time-difference-of-arrival indoor localization dataset
34 citations · 2024
📈 Most Prolific Year: 2024 (1 Papers)
🤝 Key Collaborators: 4
🏛 Institutions: University of Toronto

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago