Getting 3D Position Coordinates from an IMU Sensor on Python

Question

I am planning to acquire position in 3D cartesian coordinates from an IMU (Inertial Sensor) containing Accelerometer and Gyroscope. I'm using this to track the objects position and trajectory in 3D.

1- From my limited knowledge I was under the assumption that Accelerometer alone would be enough, resulting in acceleration in xyz axis A(Ax,Ay,Az) and would need to be integrated twice to get velocity and then position, but integrating would add an unknown constant value, this error called drift increases with time. How to remove this error?

2- Furthermore, why is there a need for gyroscope in the first place, cant we just translate the x-y-z axis acceleration to displacement, if accelerometer tells the axis of motion then why check orientation from Gyroscopes. Sorry this is a very basic question, everywhere I checked both Gyro+Accel were used but don't know why.

3- Even when stationary and not in any motion there is earth's gravitation force acting on the sensor which will always give values more than that attributed by the motion of sensor. How do you remove the gravity?

Once this has been done ill apply Kalman Filters to them to fuse them and to smooth the values. How accurate is this method for trajectory estimation of an object for environments where GPS is not an option. I'm getting the Accelerometer and Gyroscope values from arduino and then importing to Python where it will be plotted on a 3D graph updating in real time. Any help would be highly appreciated, especially links to similar codes.

Answer 1

1 - An accelerometer can be calibrated to account for some of this drift but in the end no sensor is perfect and inaccuracy will inevitably cause drift. To fix this you would need some filter such as the Kalman filter to use the accelerometer for short high frequency data, and a secondary sensor such as a camera to periodically get the absolute position and update the internal position. This is the fundamental idea behind the Kalman filter.

2 - Accelerometers aren't very good for high frequency rotational data. Just using the accelerometers data would mean the system could not differentiate between a horizontal linear acceleration and rotational position. The gyroscope is used for the high frequency data while the accelerometer is used for low frequency data to adjust and counteract the rotational drift. A Kalman filter is one possible solution to this problem and there are many great online resources explaining this.

3 - You would have to use the methods including gyro / accel sensor fusion to get the 3d orientation of the sensor and then use vector math to subtract 1g from that orientation.

You would most likely be better off looking at some online resources to get the gist of it and then using a pre-built sensor fusion system whether it be a library or an fusion system on the accelerometer (on most accelerometers today including the mpu6050). These onboard systems typically do a better job then a simple Kalman filter and can combine other sensors such as magnetometers to gain even more accuracy.