Papers
14
Total Citations
162
H-Index
6
About
Pieter van Goor is a robotics and control systems researcher whose work sits at the intersection of geometric mechanics, state estimation, and autonomous navigation. He is best known for developing the **Equivariant Filter (EqF)**, a principled and general framework for designing high-performance filters for systems whose state spaces are homogeneous manifolds equipped with transitive Lie group symmetries. This foundational contribution, introduced in 2020 and refined through subsequent publications, has accumulated over 60 citations and reshaped how researchers approach observer design for nonlinear robotic systems. A standout application of this framework is **EqVIO**, van Goor's equivariant filter for visual-inertial odometry — the problem of estimating robot trajectories from camera and IMU data — which has attracted 44 citations since its 2023 publication and demonstrated compelling real-world performance. His earlier geometric observer work for visual SLAM further established his trajectory in localization and mapping. Beyond estimation, van Goor has contributed to discrete-time equivariant design, manifold-aware Kalman filtering, and feedback linearization for over-actuated systems. His body of work, consistently grounded in mathematical rigor, offers robotics practitioners elegant tools for tackling complex, real-world navigation challenges.
Research Focus
Key Achievements
Top Papers
- 1EqVIO: An Equivariant Filter for Visual-Inertial Odometry44 citations · 2023
- 2Equivariant Filter (EqF)35 citations · 2022
- 3
- 4A Geometric Observer Design for Visual Localisation and Mapping17 citations · 2019
- 5Equivariant Filter (EqF)7 citations · 2020
- 6Equivariant Visual Odometry in the Wild6 citations · 2020
- 7Equivariant Filter Design for Discrete-time Systems6 citations · 2022
- 8
- 9A Note on the Extended Kalman Filter on a Manifold4 citations · 2023
- 10