Like a SLAM part 2.1,
Some topics here for general vibes, and a robotics site’s article on them.
Of course having sensors telling you if you’ve fallen over are useful. Of course.
The IMU just gives you the relative axes on the robot, and that can feed back to a control algorithm which uses that info to influence behaviour.
Here’s a useful Q that made me realise I must get an IMU running, perhaps. https://robotics.stackexchange.com/questions/21279/do-imu-have-value-in-slam
After reading up on IMUs, you get 3 axes: accelerometer, 6 axes: + gyroscope, 9 axes: + magnetometer. And some 10 axes ones, if it’s fancy, and has a thermometer to correct inaccuracies, etc.
6 axes gives you relative positions, 9 axes gives you absolute positions.
I happen to have a 6 axis one, from Aliexpress, from years ago. Never used it, but now I have a reason. It’s labelled GY-521. Here’s a video tutorial on putting it all together, with the tutorial link for reading.
“the 6-DoF, which was used to determine the linear velocity, angular velocity, position and orientation.” – this paper, “Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter”
You need to take these folders from the github link.
and put them in your Arduino libs folder
The github also has some code for Raspberry Pi, which I might get to next. Badabing badaboom, it actually worked first time. ( I touched the USB cable though, and needed to restart it, but that seems like something that can be prevented).
Ok so accelerometer x/y/z, temperature nice, gyroscope x/y/z
I watched these numbers as I moved it around, and at 9600 baud, it’s really slow. It’s not going to help for real time decision making.
Maybe we’ll come back to IMUs later. A bit complicated to visualise and make sense of the data, but with a visualisation, it would make odometry SLAM methods more robust.