Looks very interesting! Thanks for supplying detailed documentation.

A major gap in the existing ROS localization solution (robot_pose_ekf) has
been with respect to periodic absolute measurements, especially GPS fixes
and data from mocap systems. Does robot_localization handle these cases at
all, or provide for them to be handled with further development?

Thanks again for contributing this,

Mike


On 22 April 2014 12:47, Tom Moore <tmoore@cra.com> wrote:

>  All,
>
>
>
> I am pleased to announce the release of a new ROS package,
> robot_localization. The package estimates the state (3D pose and velocity)
> of a mobile robot through sensor fusion. Its features include:
>
>
>
> * Fusion of an arbitrary number of sensors: the nodes do not restrict the
> number of input sources. If, for example, your robot has multiple IMUs or
> multiple sources of odometry information, the nodes within
> robot_localization can support all of them.
>
> * Support for multiple ROS message types: all nodes in robot_localization
> can take in Odometry, Imu, PoseWithCovarianceStamped, or
> TwistWithCovarianceStamped messages.
>
> * Per-sensor input customization: if a given sensor message contains data
> that you don't want to include in your state estimate, robot_localization's
> nodes allow you to exclude that data on a per-sensor basis.
>
> * Continuous estimation: each node in robot_localization begins estimating
> the robot's state as soon as it receives a single measurement. If there is
> a holiday in the sensor data (i.e., a long period in which no data is
> received), the filter will continue to estimate the robot's state via a 3D
> motion model.
>
>
>
> robot_localization currently contains only one node, ekf_localization,
> which, as the name implies, employs an extended Kalman filter. New nodes,
> such as an unscented Kalman filter node, will be added as they become
> available.
>
>
>
> robot_localization is currently available for ROS Groovy, Hydro, and
> Indigo. The package's wiki page at http://wiki.ros.org/robot_localizationprovides more details on how to integrate it with your robot.
>
>
>
>
> Development of this node was funded by Charles River Analytics, Inc.
>
>
>
> -Tom
>
>
>
> ++++++++++++++++
>
> Tom Moore
>
> Software Engineer
>
> Sensor Processing & Networking
>
> Government Services
>
> Charles River Analytics Inc.
>
> 617.491.3474 x521
>
> www.cra.com
>
>
>
>
>
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>