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rosnumerical-integrationimu
Bob9710
Bob9710

Reputation: 205

How to minimize the error integrating 3D angular velocity data obtained by the IMU to get linear velocity?

have IMU sensor that gives me the raw data such as orientation, Angular and Linear acceleration. Im using ROS and doing some Gazebo UUV simulation. Furthermore, I want to get linear velocity from the raw IMU data.

So the naive integration method is as follow: The simplest method for IMU state estimation is the naive integration of the measured data. We estimate the attitude by integrating 3D angular velocity data obtained by the IMU. Assuming that the time step Δ𝑡 is small, the attitude at each time step can be incrementally calculated.

If I do integration over time there will be accumulated error and will not be accurate with the time when for example the robot makes turns. So Im looking for some methods ( ROS packages or outside ROS framework) or code that can correct that error.

Any help?

Upvotes: 2

Views: 450

Answers (1)

JWCS
JWCS

Reputation: 1211

I would first recommend that you try fitting your input sensor data into an EKF or UKF node from the robot_localization package. This package is the most used & most optimized pose estimation package in the ROS ecosystem.

It can handle 3D sensor input, but you would have to configure the parameters (there are no real defaults, all config). Besides the configuration docs above, the github has good examples of yaml parameter configurations (Ex.) (you'll want a separate file from the launch file) and example launch files (Ex.).

Upvotes: 3

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