Papers
7
Total Citations
39
H-Index
3
About
Cindy Cappelle is a researcher specializing in autonomous systems, robotic localization, and fault-tolerant cooperative navigation. Her work sits at the intersection of computer vision, multi-robot systems, and machine learning, addressing two critical challenges in modern robotics: reliable visual localization and the safety of collaborative positioning systems. Cappelle's early contributions focused on visual localization, most notably her 2019 paper combining Convolutional Networks with Locality-Sensitive Hashing for sequence-based place recognition, which has garnered 19 citations and demonstrated the power of deep image representations in real-world robotic navigation. This built upon her foundational 2016 work on multi-feature sequence matching for visual localization. More recently, her research has pivoted toward fault-tolerant cooperative localization for multi-robot systems, a domain where safety and integrity are paramount. She has explored diverse diagnostic approaches — from Jensen-Shannon divergence and decision trees to federated learning — to detect and exclude sensor faults in decentralized systems. Her comparative analyses of centralized versus federated learning architectures reflect a sophisticated understanding of scalable, safe robotics deployment. With a growing publication record spanning perception, probabilistic reasoning, and autonomous systems integrity, Cappelle represents an important voice in making multi-robot navigation both reliable and practically deployable.
Research Focus
Key Achievements
Top Papers
- 1ConvNet and LSH-Based Visual Localization Using Localized Sequence Matching19 citations · 2019
- 2
- 3
- 4
- 5
- 6
- 7