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This stack contains drivers, tools, a nonlinear position controller and imu data fusion for Ascending Technologies MAVs equipped with the AutoPilot sensor board. In contrast to asctec_drivers which communicate directly to the low level processor of the AutoPilot board (which has some limitations), this framework is based on the user-programmable high level processor of the AutoPilot board. Features are:


some acronyms are widely used in this documentation:


Installation / Dependencies

We highly recommend to use the latest version, together with the latest firmware of the LLP and a recent ros distro (hydro/indigo) supporting catkin.

  Show EOL distros: 

Please clone the following repos into your catkin_workspace/src (or add them to your rosinstall file / wstool):

Then, use catkin_build or catkin tools to build your workspace.

The following commands will fetch and compile the asctec_mav_framework stack. Version 2012 depends on ethzasl_sensor_fusion. Please refer to ethzasl_sensor_fusion for installation instructions.

# Fetch asctec_mav_framework stack
git clone git://github.com/ethz-asl/asctec_mav_framework.git asctec_mav_framework

# only if you need to use the 2011 version:
git checkout -b version2011 refs/tags/version2011

# Update ROS_PACKAGE_PATH (if necessary)
export ROS_PACKAGE_PATH=$ROS_PACKAGE_PATH:`pwd`/asctec_mav_framework

# build
rosmake asctec_mav_framework

If rosmake fails it means that you have downloaded a catkin package. In this case follow the instructions for hydro or indigo.

Change Log

December 2014

October 1st 2012

June 27th 2012

For this update, the High Level Processor needs to be flashed.

May 13th 2012

Update to be compatible with the 2012 HL SDK and LL Firmware. Currently available in the "version2012" branch (see installation instructions). This update can only be used with the 2012 LLP Firmware, it will not work with the older versions.

The state prediction part of a full EKF runs now on the HLP, which works together with ethzasl_sensor_fusion. Not only the obvious states as attitude, position and velocity are estimated, but also IMU biases, (visual) scale of the position measurement (e.g. from ethzasl_ptam) and pose/position-sensor (e.g. camera) to imu calibration. Also, a yaw measurement is not necessary anymore since this can be estimated by the EKF. More detailed information can be found here:

Further changes:

August 4th 2011

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2024-07-13 12:37