Sam Schofield
Papers
1
Total Citations
2
H-Index
1
About
Sam Schofield is a researcher advancing the field of visual-inertial odometry (VIO) through innovative dataset creation and algorithm development. His primary research areas include sensor fusion, robotics perception, and autonomous navigation, with a focus on improving the robustness of VIO systems in challenging outdoor environments. Schofield’s major contribution is the development of an improved semi-synthetic approach for generating VIO datasets, which addresses the critical limitation of obtaining high-accuracy ground truth data outdoors—where traditional motion capture systems are impractical. This work, published in 2022, has already garnered 2 citations, signaling its growing relevance in the robotics community. By enabling more realistic and reproducible benchmarking, Schofield’s methodology helps researchers validate VIO algorithms under diverse conditions, accelerating progress toward reliable autonomous navigation. His efforts bridge the gap between synthetic and real-world data, offering a practical tool for advancing sensor fusion techniques. For students and researchers exploring VIO, Schofield’s work provides a foundational resource for designing and testing robust perception systems.
Research Focus
Key Achievements
Top Papers
- 1