I am developing an Android App to track indoor position. My phone is an Google Nexus S using two sensors, the first being an Accelerometer and the second being a Gyroscope.
My idea is that from a starting reference point (a known GPS position), using the 2 sensors (accelerometer for motion and Gyro for directions) track the phone when moving. And display on a map when the user is going.
But the problem is that i have no idea how to combine both of these sensors to give me an actual position?
I have read some article on internet that talk about "kalman filter" and after some more research i found out that that filter is very very complex /too much for me) especially to code it in java for Android (cpu killer)
Does someone has some experience that he can share with me about indoor positioning system using Gyro and accelerometer?
Gyros and accelerometers are not enough.
You get position by integrating the linear acceleration twice but the error is horrible. It is useless in practice.
Here is an explanation by (Google Tech Talk) at 23:20. I highly recommend this video.
As for indoor positioning, I have found these useful:
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
Enhancing the Performance of Pedometers Using a Single Accelerometer
I have no idea how these methods would perform in real-life applications or how to turn them into a nice Android app.
A similar question is Calculating distance within a building.
I think this is a great post answering your question. This kalman filter combine data from accelerometers and gyros. Hope it helps.
For some other interesting reading on emerging indoor positioning technologies, check out this blog post from Qubulus. There are several startups and research projects trying to find a good replacement for GPS indoors. Attempts range from Dead Reckoning, to QR Codes, to light pulses, to radio fingerprinting. It looks like any viable solution will combine multiple technologies (similar to how most smartphones rely on A-GPS, where the satellite signal is assisted by cell tower multilateration).
Best of luck with your application!
I think it is too late for answer this question, but now, there is a good solution called iBeacon technology.
You can scan iBeacon devices by your smartphone, and you can get the rssi from iBeacon. So, you can calculate your position by those rssi.
To track indoor position starting from some reference point, only gyro and accelerometer is not enough. With accelerometer you can calculate speed, with gyro you can get direction of the mobile device, but to calculate indoor position you also need to have direction of movement, so for this case you need to use magnetic sensor. Such approach is called Dead Reckoning method and it's quite complex to combine all 3 sensors to get appropriate indoor position. Kalman filters allow you to smooth your measurement and filter some noise, but it's not the method to calculate indoor position. If you want to get indoor position you can try iBeacon approach and Trilateration method, there are some libraries (like this one) that already has this functionality, so you can try to investigate this method from such libs.
Related
I want to get as accurate data from the built in accelerometer in an Android phone as possible. I want to track two dimensional movement in x and y axis and even the small movements must be registered.
When I look at the data from the accelerometer / linear acceleration when the phone is flat on a table it changes a lot when i should be zero.
I have looked at Kalman filters, it seems like a good approach but I am having problems setting up a model.
1. Is a Kalman filter the way to go to get as accurate data as possible from an accelerometer?
2. Will a Kalman filter work? Maybe i have misunderstood but it seems like the acceleration or the velocity must be constant?
3. How do I set up the model for using Kalman filter? I'm having trouble understanding (among other things) what the process noise is?
A Kalman filter applies when all measurements (of acceleration in this case) are equal to the true value plus a measurement error. The measurement error is the process noise. For for the original Kalman filter to apply the noise must be normally distributed, i.e. sometimes the error will be positive, sometimes negative, and on average zero.
If you jerk your android phone quickly back and forth, there'll be large accelerations. I'd suggest recording the accelerometer readings in that kind of action, and reviewing by eye to see whether it looks like there's the readings are indeed subject to some kind of normally distributed process noise. My guess is that the answer will be "No", i.e. I expect they readings when plotted on a graph will be smooth-ish. But if they're not smooth, a Kalman filter could be useful.
If you're trying to use accelerometer readings to work out location, I think your project is doomed to failure. Acceleration is the 2nd derivative of position with respect to time, and I've never heard of anyone being able to integrate the readings with sufficient accuracy to be at all useful.
I have applied a Kalman filter successfully to GPS readings on an Android phone to improve the location estimate. See Smooth GPS data for code that implements a Kalman filter for that. I subsequently wondered whether velocity and perhaps acceleration data could be used to improve the location estimate. Although I never followed up on that idea, see https://dsp.stackexchange.com/questions/8860/more-on-kalman-filter-for-position-and-velocity for the maths that I was considering using.
The optimal way of using all the sensor inputs (GPS, accelerometer, gyroscope, etc) to get a good estimate of location is a very hard (and interesting) problem. To find out more, the key phrase to search for is "Sensor fusion". On this subject, there's an old youtube video at http://www.youtube.com/watch?v=C7JQ7Rpwn2k .
You might find this thread useful. I came across the same issues
We think the variance when lying flat might be an issue with Gimbal lock confusing the calculations but thats just a theory right now. We've also noticed the covariance in each axis alters depending on the orientation of the device, which might be gimbal lock interference too, but again just a theory
Implement a Kalman filter to smooth data from deviceOrientation API
I am just a beginner in the application development industry. I know the accelerometer can be used to return the current acceleration along three axis in meters per second squared (m/s2). But I have come to know that an accelerometer can also be use as speedometer.
I want to know how I can use accelerometer to determine distance traveled by Android device between points of interest. If it is possible, then how can I implement it? I have seen a similar question "how do I measure the distance traveled by an Iphone using accelerometer" How do I measure the distance traveled by an iPhone using the accelerometer?. But I didn't come to a point.
Thank You,
Arslan
You get position by integrating the linear acceleration twice but the error is horrible. It is useless in practice.
Here is an explanation why (Google Tech Talk) at 23:20. I highly recommend this video.
Similar questions:
track small movements of iphone with no GPS
What is the real world accuracy of phone accelerometers when used for positioning?
how to calculate phone's movement in the vertical direction from rest?
iOS: Movement Precision in 3D Space
You can combine it with GPS positioning, and filter data with Kalman filter.
GPS give rough but stable position, while accelerometer give more accerate but drifting position.
I've been doing a bit of research on a problem we are trying to solve. I think this is the best approach but please add in your opinions
We are trying to calculate reaction times in a real world driving scenario and would like to use a mobile phone as the data collection device. What we are trying to accomplish is how much acceleration and more importantly deceleration a driver exerts when exposed to certain prompts.
I found this paper that has allot of useful information Accelerometer physics
The problem is that we most likely will not have a calibration time to start at zero.. however it is assumed that the driver is starting at 0. We will use GPS positioning to locate the vehicle, tracking the time stamped location data we should calculate the time when the prompt took place then using the time stamped accelerometer data we should be able to calculate their reaction to the prompt.
This is the best way I have found to solve the problem however I'm not sure if the accelerometer data will be rendered useless because of not being able to calibrate it and also the noise seen from vibrations may be too great to use the data... Has anyone tried or used these types of methods before?
Interesting application.
You are missing an important point. You either have to implement the so-called sensor fusion yourself or use the sensor fusion provided on the platform you are using. Both Android and iPhone have one.
The TYPE_LINEAR_ACCELERATION (Android, SensorManager) or userAcceleration (iPhone) should be sufficient for you.
As for the linked PDF, don't try integrating the acceleration, you will get very poor results. Even though that answer is about position, the velocity will already be inaccurate. I would try the GPS instead.
I know it's very old question but since I am recently working on a similar project let me share what we did in our company. We simply used OBD-II dongle to get velocity of car. There are many API's that return information about vehicle.
PID010D returns speed of vehicle. I'm using this PID to calculate distance between points A and B since there is no PID to return Odometer :(
There are few libraries on github that you can find easily by search. This mine. This is not library but after run on your device you can see how it works.
this is another question about indoor tracking using inertial (smartphone + aceel + gyro)
Firstly, I would like to say that I have read almost every post on stackoverflow talking about this subject.
And I know that to track a position We will have to integrate TWICE the accel and that is very useless in a real life application because of all the drift errors...
But it turned out that I don't need to build a plane or whatever And i don't need to develop an application that have to WORK to be sold or something. I just want to realize a simple Android App that use "theoretical" concept of an Indoor tracking-
What's the possibilities?
What do we need?
Basically my phone is resting on a desk screen facing UP at a known position (0,0) if a push my phone to 2 or 3 meters and then I rotate it and push it again for 2 or 3
meters I the to see after how many meters it becomes to inaccurate an so use a tag tu recalibrate the measurements <--- That's my main question
what do I need ?
- the angle ? (ok integrating the the gyro) (i don't wanna use the compass)
- the accel? (i have)
- the velocity ? (integrating the accel)
- and the position (double accel integration)
The thing that I would like to know is How can I put this number together? Is it the right way to do it? Is there another solution (to resolve my problem, not to track someone really accurately)?
I also looked at the theory of the DCM (If I understood correctly, it will give me the orientation of the phone in 6 axes right? But what's the difference about getting the angle from the Accel or the gyro (pitch, roll etc..) ?
Thank you
Your smartphone probably has a 3-axis gyro, a 3-axis magnetometer and a 3-axis accelerometer. This is enough for a good estimation of attitude. Each has its advantages and disadvantages:
The accelerometers can measure the gravity force, it gives you the attitude of your phone, but in a horizontal position, you can't know where it's pointing. And it's very sensitive to inertial noise.
The gyroscopes are fastest and the most accurate, but its problem is the drift.
The magnetometers don't have drift and they aren't sensitive to inertial forces, but are too slow.
The combination of the three give you all advantages and no disadvantages. You must read the gyro measure faster as you can (this minimizes the drift) and then use the slow and not as accurate measure of magnetometer and accelerometer to correct them.
I leave you some links that may interest you:
A Guide to using IMU: http://www.starlino.com/imu_guide.html
DCM tutorial: http://www.starlino.com/dcm_tutorial.html
I hope I've been helpful and sorry for my bad English.
With the sensors you have, not considering computational power at this point yet, I know of only one method of position / displacement estimation. This would either involve just optical flow with the onboard camera, or the above with addidional info from fused data from accels / gyros (eg. with a Kalman-Filter) to improve accuracy. I guess OpenCV has all you need (including support for Android), so I'd start there.
Start by implementing an attitude-estimator with just accels and gyros. This will drift in yaw-axis (ie. the axis perpendicular to the ground, or rather parallel to gravity vector). This can be done with a Kalman-Filter or other algorithms. This won't be any good for position estimation, as the estimated position will drift tenths of meters away in just a couple of seconds.
Then try implementing optical flow with your camera, which is computationally expensive. Actually this alone could be a solution, but with less accuracy than with additional data from an IMU.
Good luck.
EDIT: I recently found this - it may be helpful to you. If there is not a lot of noise (due to vibration), this would work (I'm on a quadrotor UAV and it unfortunately doesn't work for me).
I am just a beginner in the application development industry. I know the accelerometer can be used to return the current acceleration along three axis in meters per second squared (m/s2). But I have come to know that an accelerometer can also be use as speedometer.
I want to know how I can use accelerometer to determine distance traveled by Android device between points of interest. If it is possible, then how can I implement it? I have seen a similar question "how do I measure the distance traveled by an Iphone using accelerometer" How do I measure the distance traveled by an iPhone using the accelerometer?. But I didn't come to a point.
Thank You,
Arslan
You get position by integrating the linear acceleration twice but the error is horrible. It is useless in practice.
Here is an explanation why (Google Tech Talk) at 23:20. I highly recommend this video.
Similar questions:
track small movements of iphone with no GPS
What is the real world accuracy of phone accelerometers when used for positioning?
how to calculate phone's movement in the vertical direction from rest?
iOS: Movement Precision in 3D Space
You can combine it with GPS positioning, and filter data with Kalman filter.
GPS give rough but stable position, while accelerometer give more accerate but drifting position.