I doing a small program using Processing, and it's basically a map of Europe and a question will ask where a certain capital is in Europe. For example, if the question is where Milan is and the person clicks on Madrid I want to use a GPS to calculate the distance between Milan and Madrid. So hopefully the output would be "You have clicked on the wrong city. You are xxx miles away from Milan".
How do I code for this?
You definitely don't need a GPS for that, in fact a GPS won't even do what you want. All you need is the coordinates of the capitals for which you will ask the location. Presumably you can get this as latitude and longitude, although since you're displaying them on the screen, perhaps you will just get their x/y coordinates from whatever image/display you are using.
Lets assume you have an x and y for a city, and a click on the screen. The distance between two points on a plane is probably something you learned in high school geometry. The equation is available on Wikipedia.
If for some reason, you need to calculate the distance between two points of latitude/longitude, that's a little more complicated, and probably not worth it, but it's doable -- in fact, the question has been asked on SO.
That should be enough to get you started. If not, you should probably flesh your question out with some details.
Related
I have a set of (slightly noisy) GPS coordinates that I want to turn into a path. How do I go about this?
I find it similar to this question, except my points are ordered. Also, the path does not need to go through the points, but just follow their general direction.
It seems that Bezier curves might be the answer, but is there a way to use Bezier curves on hundreds of points?
Q&A:
How are your points ordered They are ordered by time and attached to a travelling car. There might be data that specify that the the car is travelling backwards, but I can remove that data by requiring that all points move in a "forward" direction. So then I should have just a list of points that all go forwards in space and don't intersect with themselves.
What if we connect all the lines with straight lines It won't look pretty. I'd like for the lines to be continuous and curvy.
What about using a spline between all the points This too will make too much noise. The path will be very "jumpy". It would be better if we didn't care about going through points, but just near them.
It is a bit of heavy machinery, but you can model your GPS observations as points following a Gaussian process with Gaussian noise, where the main Gaussian process model specifies that the underlying unknown true x and y coordinates of two measurements close in time should be close, and the noise allows the observed x and y GPS measurement values to deviate a bit from the true x and y values predicted by the Gaussian process model. You can read the book "Gaussian Processes for Machine Learning" available online if you're interested. I think it's a really elegant, flexible and powerful solution, but it would take way too much space to explain it in enough detail here so you really do need to read about it from the book.
Once you've learned the most likely Gaussian process model solution, you can make predictions of x and y locations for any time point, and it will be a smooth curve, which you can then plot. It won't pass through the observed GPS locations exactly.
To make more clear what inspired this question, I'm making a mobile app where people rate in real time how nightclubs are doing and would be very good if there existed an way to make sure the person is actually there to rate if it's bad or good.
PS. I thought about comparing the coordinates of the location of the person versus the place's coordinates. Don't know if it's technically possible.
Yes you read the location from GPS. make sure you get the location only from GPS not from other locations service like cell tower or wifi.
A GPS location in most cases is acurate within 3- 30m.
Then just calculate the distance from user coordinates (latitue, longitude) to nightclubs (latiidude, longitude)
if the distance is <50m then the person is standing very near to th enntry of the club.
But dont forget inside the club you will not recieve GPS. GPS needs free view to sky.
Yes, its possible. If the app requests the GPS support and the location co-ordinates at real time. It would actually verify if the person is at there.
I am hacking a vacuum cleaner robot to control it with a microcontroller (Arduino). I want to make it more efficient when cleaning a room. For now, it just go straight and turn when it hits something.
But I have trouble finding the best algorithm or method to use to know its position in the room. I am looking for an idea that stays cheap (less than $100) and not to complex (one that don't require a PhD thesis in computer vision). I can add some discrete markers in the room if necessary.
Right now, my robot has:
One webcam
Three proximity sensors (around 1 meter range)
Compass (no used for now)
Wi-Fi
Its speed can vary if the battery is full or nearly empty
A netbook Eee PC is embedded on the robot
Do you have any idea for doing this? Does any standard method exist for these kind of problems?
Note: if this question belongs on another website, please move it, I couldn't find a better place than Stack Overflow.
The problem of figuring out a robot's position in its environment is called localization. Computer science researchers have been trying to solve this problem for many years, with limited success. One problem is that you need reasonably good sensory input to figure out where you are, and sensory input from webcams (i.e. computer vision) is far from a solved problem.
If that didn't scare you off: one of the approaches to localization that I find easiest to understand is particle filtering. The idea goes something like this:
You keep track of a bunch of particles, each of which represents one possible location in the environment.
Each particle also has an associated probability that tells you how confident you are that the particle really represents your true location in the environment.
When you start off, all of these particles might be distributed uniformly throughout your environment and be given equal probabilities. Here the robot is gray and the particles are green.
When your robot moves, you move each particle. You might also degrade each particle's probability to represent the uncertainty in how the motors actually move the robot.
When your robot observes something (e.g. a landmark seen with the webcam, a wifi signal, etc.) you can increase the probability of particles that agree with that observation.
You might also want to periodically replace the lowest-probability particles with new particles based on observations.
To decide where the robot actually is, you can either use the particle with the highest probability, the highest-probability cluster, the weighted average of all particles, etc.
If you search around a bit, you'll find plenty of examples: e.g. a video of a robot using particle filtering to determine its location in a small room.
Particle filtering is nice because it's pretty easy to understand. That makes implementing and tweaking it a little less difficult. There are other similar techniques (like Kalman filters) that are arguably more theoretically sound but can be harder to get your head around.
A QR Code poster in each room would not only make an interesting Modern art piece, but would be relatively easy to spot with the camera!
If you can place some markers in the room, using the camera could be an option. If 2 known markers have an angular displacement (left to right) then the camera and the markers lie on a circle whose radius is related to the measured angle between the markers. I don't recall the formula right off, but the arc segment (on that circle) between the markers will be twice the angle you see. If you have the markers at known height and the camera is at a fixed angle of inclination, you can compute the distance to the markers. Either of these methods alone can nail down your position given enough markers. Using both will help do it with fewer markers.
Unfortunately, those methods are imperfect due to measurement errors. You get around this by using a Kalman estimator to incorporate multiple noisy measurements to arrive at a good position estimate - you can then feed in some dead reckoning information (which is also imperfect) to refine it further. This part is goes pretty deep into math, but I'd say it's a requirement to do a great job at what you're attempting. You can do OK without it, but if you want an optimal solution (in terms of best position estimate for given input) there is no better way. If you actually want a career in autonomous robotics, this will play large in your future. (
Once you can determine your position you can cover the room in any pattern you'd like. Keep using the bump sensor to help construct a map of obstacles and then you'll need to devise a way to scan incorporating the obstacles.
Not sure if you've got the math background yet, but here is the book:
http://books.google.com/books/about/Applied_optimal_estimation.html?id=KlFrn8lpPP0C
This doesn't replace the accepted answer (which is great, thanks!) but I might recommend getting a Kinect and use that instead of your webcam, either through Microsoft's recently released official drivers or using the hacked drivers if your EeePC doesn't have Windows 7 (presumably it does not).
That way the positioning will be improved by the 3D vision. Observing landmarks will now tell you how far away the landmark is, and not just where in the visual field that landmark is located.
Regardless, the accepted answer doesn't really address how to pick out landmarks in the visual field, and simply assumes that you can. While the Kinect drivers may already have feature detection included (I'm not sure) you can also use OpenCV for detecting features in the image.
One solution would be to use a strategy similar to "flood fill" (wikipedia). To get the controller to accurately perform sweeps, it needs a sense of distance. You can calibrate your bot using the proximity sensors: e.g. run motor for 1 sec = xx change in proximity. With that info, you can move your bot for an exact distance, and continue sweeping the room using flood fill.
Assuming you are not looking for a generalised solution, you may actually know the room's shape, size, potential obstacle locations, etc. When the bot exists the factory there is no info about its future operating environment, which kind of forces it to be inefficient from the outset.
If that's you case, you can hardcode that info, and then use basic measurements (ie. rotary encoders on wheels + compass) to precisely figure out its location in the room/house. No need for wifi triangulation or crazy sensor setups in my opinion. At least for a start.
Ever considered GPS? Every position on earth has a unique GPS coordinates - with resolution of 1 to 3 metres, and doing differential GPS you can go down to sub-10 cm range - more info here:
http://en.wikipedia.org/wiki/Global_Positioning_System
And Arduino does have lots of options of GPS-modules:
http://www.arduino.cc/playground/Tutorials/GPS
After you have collected all the key coordinates points of the house, you can then write the routine for the arduino to move the robot from point to point (as collected above) - assuming it will do all those obstacles avoidance stuff.
More information can be found here:
http://www.google.com/search?q=GPS+localization+robots&num=100
And inside the list I found this - specifically for your case: Arduino + GPS + localization:
http://www.youtube.com/watch?v=u7evnfTAVyM
I was thinking about this problem too. But I don't understand why you can't just triangulate? Have two or three beacons (e.g. IR LEDs of different frequencies) and a IR rotating sensor 'eye' on a servo. You could then get an almost constant fix on your position. I expect the accuracy would be in low cm range and it would be cheap. You can then map anything you bump into easily.
Maybe you could also use any interruption in the beacon beams to plot objects that are quite far from the robot too.
You have a camera you said ? Did you consider looking at the ceiling ? There is little chance that two rooms have identical dimensions, so you can identify in which room you are, position in the room can be computed from angular distance to the borders of the ceiling and direction can probably be extracted by the position of doors.
This will require some image processing but the vacuum cleaner moving slowly to be efficiently cleaning will have enough time to compute.
Good luck !
Use Ultra Sonic Sensor HC-SR04 or similar.
As above told sense the walls distance from robot with sensors and room part with QR code.
When your are near to a wall turn 90 degree and move as width of your robot and again turn 90deg( i.e. 90 deg left turn) and again move your robot I think it will help :)
I currently have a robot with some sensors, like a GPS, an accelerometer and a compass. The thing I would like to do is my robot to reach a GPS coordinate that I enter. I wondered if any algorithm to do that already existed. I don't want a source code, which wouldn't have any point, just the procedure to follow for my robot to do so, for me to be able to understand what I do... At the moment, let's imagine that I can access the GPS coordinate everytime, so no need of a Kalman filter. I know it's unrealistic, but I would like to programm it step by step, and Kalman is the next step.
If anyone has an idea...
To get a bearing (positive angle east of north) between two lat-long points use:
bearing=mod(atan2(sin(lon2-lon1)*cos(lat2),(lat1)*sin(lat2)-sin(lat1)*cos(lat2)*cos(lon2-lon1)),2*pi)
Note - angles probably have to be in radians depending on your math package.
But for small distances you can just calculate how many meters in one degree of lat and long at your position and then treat them as flat X,Y coords.
For typical 45deg latitudes it's around 111.132 km/deg lat, 78.847 km/deg lon.
1) orient your robot toward its destination.
2) Move forward until the distance between you and your destination is increasing where you should go back to 1)
3) BUT ... if you are close enough (under a threshold), consider that you arrived at the destination.
You can use the Location class. It's BearingTo function computes the bearing you have to follow to reach another location.
There is a very nice page explaining the formulas between GPS-based distance, bearing, etc. calculation, which I have been using:
http://www.movable-type.co.uk/scripts/latlong.html
I am currently trying to do these calculations myself, and just found out that in Martin Becket answer there is an error. If you compare to the info of that webpage, you will see that the part in the middle:
(lat1)*sin(lat2)
should actually be:
cos(lat1)*sin(lat2)
Would have left a comment, but don't have the reputation yet...
If I have a point in latitude/longitude and I want to find out if it is a valid for use within a particular coordinate system, how can I do it?
As an example. Say I am working in Swiss Grid (CH 1903, EPSG 21781) and have a point representing something in London in the UK, I want to know if that point is valid in the Swiss Grid coordinate system.
Typically the conversion will work, but it may be so far outside the area that Swiss grid is good for that it is not really valid.
The context for this is that users will be importing points in latitude longitude into an application that works in a projected coordinate systme and they want to be warned when the points they are importing fall outside the projected coordinate system bounds.
But as far as I can tell, a projected coordinate system does not have any bounds.
Although the answers listed are correct - there is no concept of "bounds" in a coordinate system, there are still approaches you can use.
The EPSG database provides a latitude/longitude bounding box for each coordinate system defined. This is listed in the "Area" table, and provides a (albeit rough) bounding box in which the coordinate system is reasonable. This alone might be enough to help you determine a reasonable extent.
By checking the coordinate of your point, and the four corners in the EPSG DB, you should be able to tell whether the coordinate is (potentially) reasonable for a given projection.
You're imposing an artificial bounds upon the coordinate system by stating that there is a limit to the system itself (i.e. a point is "outside" the bounds because it falls out of the area I care about).
In that case, find the "corners" of the area you're interested in. If a point falls outside of those bounds, then you can flag the point as being outside your area of interest.
The bounds for a given coordinate system are specific to that coordinate system so there is no generic algorithm for determining if a coordinate is out of bounds.
Beyond that, being "out of bounds" is probably specific to your domain. For example, in the Swiss Grid, 400N 200E is not in Switzerland, and therefore out of bounds for the typical use of the Swiss Grid and yet still represents a real place. Is this out of bounds for your domain or not?
Sorry, but you are stuck implementing something on your own, because it really needs to be your rules. Projected coordinate systems typically don't have bounds, but they have error behavior. For a particular application, a given projected coordinate system is often only accurate enough within certain bounds.
The short answer therefore is that it really needs to be up to your application to impose those restrictions. Every coordinate system is different, and your needs will drive a lot of it. For instance, perhaps you need 100 meter accuracy, in which case the Swiss Grid might be adequate for a fairly large area. If you need 10 meter accuracy, the errors the build up on the edge cases will become significant much faster, and therefore your bounds must be tighter.
Using a "four-corners-bounding-box" approach will work the bulk of the time, but it's not universal. Some projections bounds are better expressed as "radius from the central point", and it wouldn't shock me if some where "distance from the prime meridian". It's all a matter of how the errors build up for a given projection.
The EPSG and custom projections are available at:
http://www.spatialreference.org/
Search the site for your EPSG code and you can see the coordinates listed.