Médéric Fourmy

I am a Postdoc between LAAS-CNRS in Gepetto team working with Nicolas Mansard and IRI working with Joan Solà.

Main main research topic is at the interface between factor graph state estimation and legged robotics. I proposed new formulations for measurement models of proprioceptive and exteroceptive sensors and implemented solutions on quadruped and humanoid robots. The goal of this project is to fuse all sensor modalities in a single tightly coupled estimator to provide a globally consistent robot pose estimation that can be used for both estimation and planning.

This video presents our work on tightly coupled base and centroidal state estimation for legged robots:

I also collaborated with Cesar Debeunne to build a object level Visual Inertial SLAM system based on CosyPose.

I received an aerospace engineering degree from ISAE Supaero with speciality in data science, robotics and operational research. I completed my Master Thesis as part of the Ocean System Laboratory, working on visual servoing and force control under the supervision of Yvan Petillot at Heriot Watt University, Edinburgh, UK.

Publications

AbsoluteLoca

Absolute humanoid localization and mapping based on IMU Lie group and fiducial markers

Fourmy, Médéric, Atchuthan, Dinesh, Mansard, Nicolas, Sola, Joan, and Flayols, Thomas

In IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) 2019

Abs HTML PDF

Current locomotion algorithms in structured (indoor) 3D environments require an accurate localization. The several and diverse sensors typically embedded on legged robots (IMU, coders, vision and/or LIDARS) should make it possible if properly fused. Yet this is a difficult task due to the heterogeneity of these sensors and the real-time requirement of the control. While previous works were using staggered approaches (odometry at high frequency, sparsely corrected from vision and LIDAR localization), the recent progress in optimal estimation, in particular in visual-inertial localization, is paving the way to a holistic fusion. This paper is a contribution in this direction. We propose to quantify how a visual-inertial navigation system can accurately localize a humanoid robot in a 3D indoor environment tagged with fiducial markers. We introduce a theoretical contribution strengthening the formulation of Forster’s IMU pre-integration, a practical contribution to avoid possible ambiguity raised by pose estimation of fiducial markers, and an experimental contribution on a humanoid dataset with ground truth. Our system is able to localize the robot with less than 2 cm errors once the environment is properly mapped. This would naturally extend to additional measurements corresponding to leg odometry (kinematic factors) thanks to the genericity of the proposed pre-integration algebra.
ContactPreint

Contact Forces Preintegration for Estimation in Legged Robotics using Factor Graphs

Fourmy, Médéric, Flayols, Thomas, Léziart, Pierre-Alexandre, Mansard, Nicolas, and Solà, Joan

In IEEE International Conference on Robotics and Automation (ICRA) 2021

Abs HTML PDF

State estimation, in particular estimation of the base position, orientation and velocity, plays a big role in the efficiency of legged robot stabilization. The estimation of the base state is particularly important because of its strong correlation with the underactuated dynamics, i.e. the evolution of center of mass and angular momentum. Yet this estimation is typically done in two phases, first estimating the base state, then reconstructing the center of mass from the robot model. The underactuated dynamics is indeed not properly observed, and any bias in the model would not be corrected from the sensors. While it has already been observed that force measurements make such a bias observable, these are often only used for a binary estimation of the contact state. In this paper, we propose to simultaneously estimate the base and the underactuation state by using all measurements simultaneously. To this end, we propose several contributions to implement a complete state estimator using factor graphs. Contact forces altering the underactuated dynamics are pre-integrated using a novel adaptation of the IMU pre-integration method, which constitutes the principal contribution. IMU pre-integration is also used to measure the positional motion of the base. Encoder measurements are then participating to the estimation in two ways: by providing leg odometry displacements, contributing to the observability of IMU biases; and by relating the positional and centroidal states, thus connecting the whole graph and producing a tightly-coupled whole-body estimator. The validity of the approach is demonstrated on real data captured by the Solo12 quadruped robot
WOLF

WOLF: A modular estimation framework for robotics based on factor graphs

Sola, Joan, Vallve, Joan, Casals, Joaquim, Deray, Jeremie, Fourmy, Médéric, Atchuthan, Dinesh, Corominas-Murtra, Andreu, and Andrade-Cetto, Juan

IEEE Robotics and Automation Letters 2022

Abs PDF

This paper introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF extends the applications of factor graphs from the typical problems of SLAM and odometry to a general estimation framework able to handle self-calibration, model identification, or the observation of dynamic quantities other than localization. WOLF produces high throughput estimates at sensor rates up to the kHz range, which can be used for feedback control of highly dynamic robots such as humanoids, quadrupeds or aerial manipulators. Departing from the factor graph paradigm, the architecture of WOLF allows for a modular yet tightly-coupled estimator. Modularity is based on plugins that are loaded at runtime. Then, integration is achieved simply through YAML files, allowing users to configure a wide range of applications without the need of writing or compiling code. Synchronization of incoming data and their processing into a unique factor graph is achieved through a decentralized strategy of frame creation and joining. Most algorithmic assets are coded as abstract algorithms in base classes with varying levels of specialization. Overall, these assets allow for coherent processing and favor code reusability and scalability. WOLF can be interfaced with different solvers, and we provide a wrapper to Google Ceres. Likewise, we offer ROS integration, providing a generic ROS node and specialized packages with subscribers and publishers. WOLF is made publicly available and open to collaboration.
CosySLAM

CosySlam: investigating object-level SLAM for detecting locomotion surfaces

Debeunne, César, Fourmy, Médéric, Labbé, Yann, Léziart, Pierre-Alexandre, Saurel, Guilhem, Solà, Joan, and Mansard, Nicolas

Abs PDF

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM. We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end). We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM.