Table of Contents
Pronto is an efficient EKF state estimator for inertial and sensory motion estimation. It 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.
Performance: With inertial and kinematic input (i.e. no LIDAR input) the drift rate of the estimator is 2cm per 10 steps travelled. We estimate this to be 10 times better than the estimator provided by Boston Dynamics. With the closed-loop LIDAR module, drift is removed entirely.
It has since been adapted to estimate the motion of the NASA Valkyrie robot at University of Edinburgh - just as reliably. As well as the source code we also provide some data samples to demonstrate the algorithm working with both of these two humanoid robots walking and manipulating.
Pronto has been used with a variety of inputs from sensors such as IMUs (Microstrain and Kearfott), laser ranger finders, cameras and joint kinematics.
The algorithms are built primarily in C/C++. A number of the local dependencies were developed for our robotics projects. The software repository consists of two main modules:
- externals: required modules such as Eigen, octomap and visualization tools
- pronto: the core estimator library.
These compile instructions were tested on a fresh Ubuntu 14.04 install, but is likely to work on other versions of Linux and MacOS.
Install these common system dependencies:
apt-get install git build-essential libglib2.0-dev openjdk-6-jdk python-dev cmake gtk-doc-tools libgtkmm-2.4-dev freeglut3-dev libjpeg-dev libtinyxml-dev libboost-thread-dev libgtk2.0-dev python-gtk2 mesa-common-dev libgl1-mesa-dev libglu1-mesa-dev libqwt-dev
Check out the source code using git:
git clone https://github.com/ipab-slmc/pronto-distro.git cd pronto-distro git submodule update --init --recursive
Then start compiling:
make
The compile time is about 4 mins.
Signal Scope is a lightweight signal plotting tool. Its invaluable for debugging systems such as Pronto. It was developed for the MIT DRC team by Pat Marion.
Launch it by pointing it to a python config file in config/signal_scope. There are many examples of using it in signal_scope/examples.
Test LCM logs for both BDI Atlas (v5) and NASA Valkyrie can be downloaded from the following location. Also included are videos showing the output of the estimator.
http://www.robots.ox.ac.uk/~mfallon/share/pronto_logs/
The following commands will process the respective log files.
se-fusion -P val/robot.cfg -U val_description/urdf/valkyrie_sim.urdf -L raluca-turning-180deg-snippet.lcmlog robot_model_publisher val_description/urdf/valkyrie_sim.urdf se-state-sync-simple pronto-viewer -c val/robot.cfg
se-fusion -P atlas/robot.cfg -U atlas_v5/model_LR_RR.urdf -L 20160315-walking.lcmlog robot_model_publisher model_LR_RR.urdf se-state-sync-simple pronto-viewer -c val/robot.cfg
Some notes:
- All the state estimation is done in se-fusion. It listens to messages published from the log and produces POSE_BODY - the position and orientation of the robot's pelvis.
- pronto-viewer is a GUI showing the sensor data and the position of the robot.
- Make sure that POSE_BODY and STATE_ESTIMATOR_STATE are disabled (they were the position generated during the actual experiment)
- bot-spy is a tool for inspecting the messages.
- lcm-logplayer-gui is a gui based tool for playing back lcm logs (surprise!), we use it a lot to simulate live receipt of data. The logs can also be processed by playing back the logs from the tool.
Having tried out the test examples. How can you use Pronto with your robot?
Getting Started: To use the estimator on your robot, you simply need to provide the required inputs to our system:
- IMU measurements of type ins_t.lcm (ROS: sensor_msgs/Imu) * Also support the KVH 1750 IMU which is in the Atlas
- Joint States of type joint_states_t.lcm (ROS: sensor_msgs/JointState)
- Force Torque sensor of type six_axis_force_torque_array_t.lcm (ROS: geometry_msgs/WrenchStamped)
Pronto will output:
- POSE_BODY - the position, orientation and velocity of the robot's pelvis
I have provided a skeleton translator which I assume you will need to modify to use in your system. Get in touch if you would like some help in doing this.
On ROS Indigo the follow contents should be added to bashrc:
export PATH=/home/drc/pronto-distro/build/bin:$PATH source /opt/ros/indigo/setup.bash export PKG_CONFIG_PATH=<your-path-to>/pronto-distro/build/lib/pkgconfig/:<insert-path-to>/pronto-distro/build/lib64/pkgconfig/:$PKG_CONFIG_PATH export LD_LIBRARY_PATH=<your-path-to>/pronto-distro/build/lib/:<insert-path-to>/pronto-distro/build/lib64/:$LD_LIBRARY_PATH export DRC_BASE=<your-path-to>/pronto-distro
The package can then be compiled using catkin:
cd <insert-path-to>/pronto-distro/pronto-lcm-ros-translators catkin_make source <insert-path-to>/pronto-distro/pronto-lcm-ros-translators/devel/setup.bash
And then a translators can be run in each direction:
rosrun pronto_translators ros2lcm rosrun pronto_translators lcm2ros
Tested on Ubuntu 14.04 with ROS Indigo.
We have successfully used Pronto with 4 other bipeds (including NASA Valkyrie) and a quadruped. If you are interested in using the estimator with your own controller, please get in touch.
At MIT and Edinburgh we use Pronto as our 333Hz Drake controller in a high-rate control loop. Latency and relability have allowed us to demonstrate challenging locomotion using the Atlas robot.
Pronto was originally developed for Micro Aerial Vehicle state estimation.
Micro aerial vehicle estimation using Pronto
Log files demonstrating flight with Quadrotators and Fixed-wing RC Planes can be provided on request.
Supported sensor of interest to aerial flight:
- GPS - x, y, z
- Vicon - x, y, z and orientation
- Laser Scanmatcher - x, y, z and yaw or velocity and yaw rate
- Optical Flow - velocity, yaw rate (downward facing camera)
- Airspeed - forward velocity
- Altimeter - z
- Sideslip - lateral velocity
And example configuration for these sensors is in docs/aerial_sensors_example.cfg
Currently Pronto uses LCM to receive data and to publish output.
Lightweight Communications and Marshalling (LCM) is a tool for efficient multi-process message passing originally developed at MIT for the DARPA Urban Challenge.
To those familiar with ROS, it serves the same purpose as the message passing in ROS: messages are typed data structures and code is compiled to allow C/C++, python and Java bindings. Data is received in a process via network communication and event-based function callbacks.
If you are interested in a native ROS application, please get in touch.
- State Estimation for Aggressive Flight in GPS-Denied Environments Using Onboard Sensing, A. Bry, A. Bachrach, N. Roy, ICRA 2012.
- Drift-Free Humanoid State Estimation fusing Kinematic, Inertial and LIDAR sensing, M. Fallon, M. Antone, N. Roy, S. Teller. Humanoids 2014.
Originally Developed by Adam Bry, Abe Bachrach and Nicholas Roy of the MIT Robust Robotics Group.
Extended to support humanoid motion by Maurice Fallon with the help of the MIT DARPA Robotics Challenge Team.
Additional contributions from:
- Andy Barry
- Pat Marion
The License information is available in the LICENSE file attached to this document.
Maurice Fallon, Feb 2015. mfallon@robots.ox.ac.uk