Papers

16

Total Citations

2,643

H-Index

12

About

Stephen Se is a pioneering researcher in computer vision and mobile robotics, best known for his groundbreaking contributions to simultaneous localization and mapping (SLAM) using vision-based techniques. His most influential work, spanning the early 2000s, introduced scale-invariant visual landmarks as a robust alternative to traditional sensor-based approaches like laser range finders and sonar, enabling robots to localize and map unmodified environments with remarkable accuracy. These foundational papers have collectively garnered well over 1,700 citations, establishing Se as a central figure in visual SLAM research. Se's work extended beyond indoor robotics to tackle the demanding challenges of planetary exploration, demonstrating how vision-based modeling and localization could support autonomous rovers traversing kilometers of unknown terrain. His research into global localization — solving the so-called "kidnapped robot problem" — further broadened the practical applicability of his methods. Later work revealed a versatile research profile, including photo-realistic 3D model reconstruction using handheld devices and innovative markerless motion tracking for awake animals in medical imaging contexts. Early contributions also addressed assistive technology through robotic sensing for the partially sighted, reflecting a consistent commitment to applying intelligent sensing systems to real-world human and scientific challenges.

Research Focus

Key Achievements

12
H-Index
16
Papers
2,643
Total Citations
165
Avg Citations/Paper
🏆 Most Cited Paper
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks
779 citations · 2002
📈 Most Prolific Year: 2002 (4 Papers)
🤝 Key Collaborators: 26
🏛 Institutions: Brampton Civic Hospital, University of British Columbia, Air Canada, University of Oxford

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago