it's now the standard practices to fuse the measurements from accelerometers and gyro through Kalman filter, for applications like self-balancing 2-wheel carts: for example: http://www.mouser.com/applications/sensor_solutions_mems/
accelerometer gives a reading of the tilt angle through arctan(a_x/a_y). it's very confusing to use the term "acceleration" here, since what it really means is the projection of gravity along the devices axis (though I understand that , physically, gravity is really just acceleration ).
here is the big problem: when the cart is trying to move, the motor drives the cart and creates a non-trivial acceleration in horizontal direction, this would make the a_x no longer a just projection of gravity along the device x-axis. in fact it would make the measured tilt angle appear larger. how is this handled? I guess given the maturity of Segway, there must be some existing ways to handle it. anybody has some pointers?
thanks
Yang
You are absolutely right. You can estimate pitch and roll angles using projection of gravity vector. You can obtain gravity vector by utilizing motionless accelerometer, but if accelerometer moves, then it measures gravity + linear component of acceleration and the main problem here is to sequester gravity component from linear accelerations. The best way to do it is to pass the accelerometer signal through Low-Pass filter.
Please refer to
Low-Pass Filter: The Basics or
Android Accelerometer: Low-Pass Filter Estimated Linear Acceleration
to learn more about Low-Pass filter.
Sensor fusion algorithm should be interesting for you as well.
Related
I was wondering if Lucas-Kanade Optical flow would be the best way to estimate the velocity of a baseball/cricketball being hit. I want to get a 3d velocity vector of the initial velocity of the ball to plug into a physics engine. Would I need 2 cameras, 1 for the x, y component and another for x/y, z Or can it be done with 1?
I wouldn't rely on optical flow, this will be too noisy. Much more reliable to take the positions in two successive frames. Two cameras, stereoscopy.
A computer-tomography device has a roentgen matrix of 20x500 dots with the resolution of 2mm in each direction. This matrix is rotating around a belt, shich transports items to be analysed. A special reconstruction algorithm produces 3D model of the items from many-many matixes captured from all 360 perspectives ( one image per 1° angle).
The problem is, the reconstruction algorithm is very sensitive to the belt speed/position. Measuring the belt position requires quite complicated and expensive positining sensors and very fine mechanics.
I wonder if it is possible to calculate the belt velocity using the roentgen-image itself. It has a width of 40mm and should be sufficient for capturing the movement. The problem is, the movement is always in 2 directions - rotation and X (belt). For those working in CT-area, are you aware of some applications/publishings about such direct measurement of the belt/table velocity?
P.S.: It is not vor medical application.
Hmm, interesting idea.
Are you doing a full 180 degree for the reconstruction? I'd go with the 0 and 180 degree cone beam images. They should be approximately the same, minus some non-linear stuff, artifacts, Poisson noise and difference in 'shadows' and scattering due to perspective.
You could translate the 180 image along the negative x-axis, to the direction opposite of the movement. You could then subtract images at some suitable intervals along this axis. When the absolute of the sum hits a minimum the translation should be approx at the distance the object has moved between 0 and 180, as the mirror images cancel each other out partially.
This could obviously be ruined by artifacts and wonkily shaped heavy objects. Still, worth a try. I'm assuming your voltage is pretty high if you are doing industrial stuff.
EDIT: "A special reconstruction algorithm produces 3D model of the items from many-many matixes captured from all 360 perspectives ( one image per 1° angle)."
So yes, you are using 180+ degrees. You could then perhaps use multiple opposite images for a more robust routine. How do you get the full circle? Are you shooting through the belt?
I know that a tachometer is an instrument that measures the rotation speed of a shaft or disk.
A tachometer can be used for the following:
1. An NXT motor has a built in tachometer that keeps track of the current angle (in degrees) of the motor axle.
2. Is used to determine the speed of the wheels.
What else could it be used for?
A tachometer used to keep track of an angle of any rotational axis, like the one you mention in your question, has a more general parent name that might be more searchable: an encoder. Encoders are used to track angles of more than just wheels; they can track robotic arm joint angles, camera angles, etc. Any moveable joint might be fitted with an encoder so that a robot can measure where it is in space. For further Googling and learning, the computation of turning a model of a robot + the data from that robot's encoders into an end position is called kinematics.
I want to find the cardinal direction accelerated by an iphone. I thought I could just use the accelerometer to do this, however, as you can see from the picture below the accelerometers axes are defined by the device orientation.
I figured that if i used the gyroscope to correct for yaw, spin, rotation then I could get a more accurate reading and not have to hold the phone in the same orientation during movement.
But this still does not tell me what cardinal direction the iphone is moving in. For that I would also have to use the the magnetometer.
Can anybody tell me how to use a three sensor readings to find the cardinal direction accelerated in? I dont even know where to start. I dont even know if the phone takes these measurements at the same rates of time either.
Taking the cross product of the magnetometer vector with the "down" vector will give you a horizontal magnetic east/west vector; from that, a second cross product gets the magnetic north/south vector. That's the easy part.
The harder problem is tracking the "down" vector effectively. If you integrate the accelerometers over time, you can filter out the motion of a hand-held mobile device, to get the persistent direction of gravity. Or, you could, if your device weren't rotating at the same time...
That's where the rate gyros come in: the gyros can let you compensate for the dynamic rotation of the hand-held device, so you can track your gravity in real-time. The classic way to do this is called a Kalman filter, which can integrate (both literally and figuratively) multiple data sources in order to evaluate the most likely state of your system.
A Kalman filter requires a mathematical model both of your physical system, and of the sensors that observe it; each of these models must be both accurate and "sufficiently linear" for the Kalman filter to work properly. As it happens, the iphone/accelerometer/gyro system is in fact sufficiently linear.
The Kalman filter uses both calculus and linear algebra, so if you're rolling your own, you will need a certain amount of math.
Also, as a practical matter, you should understand that physical sensors typically have offsets that need to be compensated for -- in particular, you need to pay attention to the rate gyro offsets in this kind of inertial navigation system, or your tracker will never stabilize. This means you will need to add your rate gyro offsets to your Kalman state vector and system model.
Iphone accelerometer provides user acceleration and gravity reading? It's clear from the apple guide the user acceleration means, the acceleration than the phone get's from the user. Then what is the meaning of gravity reading? Anyway they have mention total acceleration is the addition of both! I m confused with gravity reading, Can anyone explain me the theory behind this?
Thank you in advance!
The accelerometer measures the sum (user acceleration + gravity). Unfortunately it is of little use, in your application you typically need one or the other. So it is split into components (into user acceleration and gravity) with a sophisticated procedure called sensor fusion.
Gravity points downwards, corresponds to the gravitation. It tells you where downwards is. You can compute tilt angles from it, which may be handy in developing tilt games.
The user acceleration is handy when you are trying to figure out how the phone is shaken. It good for shaking games.
See also accelerometers uses- smartphone
Gravity is an acceleration which is constant an downward. It is indistingishable along its vector from any other constant acceleration. Rotating the phone is going to change the contribution of gravity to each of the acceleration axis proportionate to angle with the ground. Gravity being constant, any change in combined vector length of the three axis should be due to user acceleration.