Axel Barrau

About

Axel Barrau is a prominent researcher at the intersection of geometric estimation theory, robotics, and autonomous navigation, best known for his foundational contributions to invariant filtering on Lie groups. His most influential work, "The Invariant Extended Kalman Filter as a Stable Observer" (2016, 588 citations), rigorously established the theoretical convergence guarantees of the Invariant Extended Kalman Filter (IEKF), transforming it from a heuristic engineering tool into a mathematically principled observer framework. Building on this, his 2017 survey "Invariant Kalman Filtering" (213 citations) brought these geometric ideas to a broad robotics and control audience, cementing the field's foundations. Barrau has consistently pushed state estimation into real-world applications, developing invariant filters for visual-inertial SLAM, addressing EKF consistency issues in navigation, and advancing ICP covariance estimation for point cloud fusion. More recently, he has bridged classical estimation with modern machine learning, proposing deep learning methods for IMU gyroscope denoising that achieve state-of-the-art open-loop attitude estimation. With over 1,200 cumulative citations, his work profoundly influences how autonomous systems achieve reliable, geometrically consistent localization in GPS-denied environments, making him an essential reference for researchers in robotics, sensor fusion, and nonlinear estimation.

Research Focus

Key Achievements

Top Papers

1. 1
   The Invariant Extended Kalman Filter as a Stable Observer

   588 citations · 2016
2. 2
   Invariant Kalman Filtering

   213 citations · 2017
3. 3
   Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude\n Estimation

   148 citations · 2020
4. 4
   Invariant Kalman Filtering for Visual Inertial SLAM

   74 citations · 2018
5. 5
   Exploiting Symmetries to Design EKFs With Consistency Properties for Navigation and SLAM

   70 citations · 2018
6. 6
   A New Approach to 3D ICP Covariance Estimation

   65 citations · 2020
7. 7
   Linear observed systems on groups

   39 citations · 2019
8. 8
   Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation

   16 citations · 2020
9. 9
   Unscented Kalman Filtering on Lie Groups for Fusion of IMU and Monocular Vision

   6 citations · 2017
10. 10
    RINS-W: Robust Inertial Navigation System on Wheels

    4 citations · 2019

Key Collaborators

Contact & Links