David Wisth

University of Oxford, Science Oxford

Papers

9

Total Citations

559

H-Index

7

About

David Wisth is a robotics researcher specializing in state estimation, sensor fusion, and autonomous navigation for legged and aerial robots operating in challenging environments. His work centers on developing robust odometry systems that enable robots to operate reliably across diverse and demanding terrains where individual sensors alone would fail. Wisth's most significant contributions lie in multi-modal sensor fusion for legged robotics. His VILENS system (160 citations) pioneered the tight integration of visual, inertial, lidar, and leg odometry within a unified factor graph framework, achieving reliable navigation across all terrain types. Complementing this, his unified multi-modal landmark tracking approach (141 citations) demonstrated real-time joint optimization of visual, lidar, and inertial data, pushing the boundaries of what tightly coupled odometry systems can achieve on mobile platforms. Beyond state estimation, Wisth was a core contributor to Team CERBERUS, the winning team of the prestigious DARPA Subterranean Challenge in 2021 — a landmark achievement in autonomous robotic exploration. His work on sewer inspection with legged robots (70 citations) further demonstrates his commitment to real-world applications, addressing critical infrastructure monitoring challenges. With over 550 total citations, Wisth has established himself as a leading voice in robust, perception-driven autonomy for next-generation robotic systems.

Research Focus

Key Achievements

7
H-Index
9
Papers
559
Total Citations
62
Avg Citations/Paper
🏆 Most Cited Paper
VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots
160 citations · 2022
📈 Most Prolific Year: 2022 (5 Papers)
🤝 Key Collaborators: 65
🏛 Institutions: University of Oxford, Science Oxford

Top Papers

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

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago