pronto

Updates

November 2025: Pronto has been updated to ROS2 with the support of Francesco Iotti and colleagues in Università di Pisa. The main branch of the repo targets ROS 2 Humble.

For the ROS 1 version, check out the ros1 branch of this repository.

Introduction

Pronto is an efficient, versatile and modular EKF state estimator for both proprioceptive (inertial, kinematics) and exteroceptive (LIDAR, camera) sensor fusion. It has been used with a variety of inputs from sensors such as IMUs (Microstrain, KVH, XSense), LIDAR (Hokuyo, Velodyne), cameras (Carnegie Robotics Multisense SL, Intel RealSense) and joint kinematics.

Legged Robots

Pronto provided the state estimate that was used by MIT DRC team in the DARPA Robotics Challenge to estimate the position and motion of the Boston Dynamics Atlas robot.

Pronto on Atlas

Since then, it has been adapted to estimate the motion of the NASA Valkyrie robot at the University of Edinburgh, the HyQ quadruped robot at the Istituto Italiano di Tecnologia, and the ANYmal quadruped robot at the University of Oxford.

Pronto on HyQ

Micro Aerial Vehicles

Pronto was originally developed for Micro Aerial Vehicle state estimation. The modules specific to MAVs (e.g., altimeter, GPS) are not currently supported.

Software Overview

The algorithms (and their ROS wrappers) are written in C/C++ and organized as ros packages. The repository consists of the following main modules:

• pronto_core: core libraries that implement the filter, the state, and basic measurement modules (e.g., IMU, pose update)
• pronto_biped leg odometry measurement modules for humanoid robots (tested on Atlas and Valkyrie)
• pronto_quadruped leg odometry libraries for a quadruped robot (tested on HyQ and ANYmal)
• pronto_quadruped_commons abstract interfaces to perform leg odometry on a quadruped robot. This is a fork of the iit_commons package (see here).
• *_ros ROS wrappers of the above modules
• other support packages for filtering

For further details, refer to this.

Dependencies

Pronto depends on Eigen, Boost and Pinocchio.

System Requirements

The target operating system is Ubuntu 22.04 with ROS 2 Humble. Other versions of Ubuntu/ROS might work but they are not actively supported or tested.

Building the Code

Pronto is organized as a collection of ROS packages. To build the code, run

colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release -DCMAKE_EXPORT_COMPILE_COMMANDS=ON

Then, source the workspace:

source install/setup.bash

Usage

Start Estimator

The estimator node can be started with

ros2 launch  pronto_ros2_node pronto_node.launch.py xacro_pkg:=<robot_description_package> xacro_name:=<xacro_file_name> config_name:=<configuration_file name>

Where the arguments within <...> must be replaced with the actual values.

The config file must be added to the config folder in pronto_ros2_node package.

Benchmarking

The Benchmarking launch allows the user to start a list of pronto instance to compare the obtained estimations. The input is a bag contains all the filter measures input, or at least it should be consistent with the configuration files. The configuration files has to be placed into the config folder and named omnicar_tune_i.yaml, each file is associated with a pronto instance. The launch file contains the global variable to set the bag name and the list of instance number, subset of the configuration files' list. To start the filters the command line is:

ros2 launch pronto_ros2_node bench_pronto.launch.py

Robot Implementation Example

To learn how to use Pronto on your robot, you can have a look at this repository, which contains a full implementation on the ANYmal quadruped robot.

Publications

If you use part of this work in academic context, please cite the following publication:

M. Camurri, M. Ramezani, S. Nobili, M. Fallon Pronto: A Multi-Sensor State Estimator for Legged Robots in Real-World Scenarios in Frontiers on Robotics and AI, 2020 (PDF) DOI: 10.3389/frobt.2020.00068

@article{camurri2020frontiers,
  author = {Camurri, Marco and Ramezani, Milad and Nobili, Simona and Fallon, Maurice},
  title = {{Pronto: A Multi-Sensor State Estimator for Legged Robots in Real-World Scenarios}},
  journal = {Frontiers in Robotics and AI},
  volume = {7},
  number = {68},
  pages = {1--18},
  year = {2020},
  url = {https://www.frontiersin.org/article/10.3389/frobt.2020.00068},
  doi = {10.3389/frobt.2020.00068},
  issn = {2296-9144}
}

Previous publications

S. Nobili, M. Camurri, V. Barasuol, M. Focchi, D.G. Caldwell, C. Semini, M. Fallon Heterogeneous Sensor Fusion for Accurate State Estimation of Dynamic Legged Robots in Proceedings of Robotics: Science and Systems XIII, 2017 (PDF) DOI: 10.15607/RSS.2017.XIII.007

@inproceedings{nobili2017rss,
    author = {Simona Nobili AND Marco Camurri AND Victor Barasuol AND Michele Focchi AND Darwin Caldwell AND Claudio Semini AND Maurice Fallon},
    title = {{Heterogeneous Sensor Fusion for Accurate State Estimation of Dynamic Legged Robots}},
    booktitle = {Proceedings of Robotics: Science and Systems},
    year = {2017},
    address = {Cambridge, Massachusetts},
    month = {July},
    doi = {10.15607/RSS.2017.XIII.007}
}

M. Camurri, M. Fallon, S. Bazeille, A. Radulescu, V. Barasuol, D.G. Caldwell, C. Semini Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots in IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 1023-1030, April 2017 (PDF) DOI: 10.1109/LRA.2017.2652491

@article{camurri2017ral,
      author={M. {Camurri} and M. {Fallon} and S. {Bazeille} and A. {Radulescu} and V. {Barasuol} and D. G. {Caldwell} and C. {Semini}},
      journal={IEEE Robotics and Automation Letters},
      title={{Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots}},
      year = {2017},
      volume = {2},
      number = {2},
      pages = {1023-1030},
      doi = {10.1109/LRA.2017.2652491},
      ISSN = {2377-3766},
      month = {April}}

M. Fallon, M. Antone, N. Roy, S. Teller Drift-Free Humanoid State Estimation fusing Kinematic, Inertial and LIDAR sensing 2014 IEEE-RAS International Conference on Humanoid Robots (PDF) DOI:10.1109/HUMANOIDS.2014.7041346

@inproceedings{fallon2014humanoids,
author={M. F. {Fallón} and M. {Antone} and N. {Roy} and S. {Teller}},
booktitle={2014 IEEE-RAS International Conference on Humanoid Robots},
title={Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing},
year={2014},
volume={},
number={},
pages={112-119},
doi={10.1109/HUMANOIDS.2014.7041346},
ISSN={},
month={Nov},}

A. Bry, A. Bachrach, N. Roy State Estimation for Aggressive Flight in GPS-Denied Environments Using Onboard Sensing 2012 IEEE International Conference on Robotics and Automation (PDF) DOI:10.1109/ICRA.2012.6225295

@inproceedings{bry2012icra,
author={A. {Bry} and A. {Bachrach} and N. {Roy}},
booktitle={2012 IEEE International Conference on Robotics and Automation},
title={State estimation for aggressive flight in GPS-denied environments using onboard sensing},
year={2012},
volume={},
number={},
pages={1-8},
doi={10.1109/ICRA.2012.6225295},
ISSN={},
month={May},}

Credits

• Originally Developed by Adam Bry, Abe Bachrach and Nicholas Roy of the MIT Robust Robotics Group for Micro Aerial Vehicles.

• Extended to support humanoid motion by Maurice Fallon with the help of the MIT DARPA Robotics Challenge Team.

• Support for quadruped robots, full ROS 1 conversion and logo design by Marco Camurri (IIT Dynamic Legged System Lab and ORI Dynamic Robot Systems Group), currently at IDRA Research group, University of Trento

• Ported to ROS 2 by Jacopo Cioni, Francesco Iotti, and Davide De Benedittis from the University of Pisa, under the supervision of Franco Angelini and Manolo Garabini.

Additional contributors

Andy Barry, Pat Marion, Dehann Fourie, Marco Frigerio, Michele Focchi, Benoit Casseau, Russell Buchanan, Wolfgang Merkt

License

Pronto is released under the LGPL v2.1 license. Please see the LICENSE file attached to this document for more information.