I saw a linear_quadratic_regulator.h includes infinite horizon fixed point LQR. I am wondering if there is a LQR for finite horizon optimal tracking. I saw some information about tvlqr on the website, but is there any news on it?
Not yet, I'm afraid, but it's coming very soon.
https://github.com/RobotLocomotion/drake/issues/9013
Russ
Related
What is the best strategy for determining a user's location from a series of GPS fixes, once they are considered not to be moving?
When tracking a user, if they should stop moving there will subsequently be a sequence of fixes roughly in the same location.
If possible I would like to not just use the last fix, but also take into consideration previous fixes so as to calculate a more accurate position for them.
Factors that I would have thought need to be considered:
The best way to determine a user is stationary (from experience speed from the GPS fixes is not sufficiently reliable)
Each fix has an accuracy, how can this be factored in?
Are there well established algorithms/libraries that could be used?
Any suggestions greatly appreciated
What's wrong with taking the average?
If you want to take uncertainty into account, use a weighted average. Or a trimmed one, discarding those measurements that deviate most.
But it is a known fact that other factors such as reflections on buildings, can have a much larger effect on the accuracy.
Nevertheless, this is not so much a programming question, but one that needs GPS expertise. You are better off reading expert literature than asking random internet users for their opinion.
Background: I am trying to find a list of floating point parameters for a low level controller that will lead to balance of a robot while it is walking.
Question: Can anybody recommend me any local search algorithms that will perform well for the domain I just described? The main criteria for me is the speed of convergence to the right solution.
Any help will be greatly appreciated!
P.S. Also, I conducted some research and found out that "Evolutianry
Strategy" algorithms are a good fit for continuous state space. However, I am not entirely sure, if they will fit well my particular problem.
More info: I am trying to optimize 8 parameters (although it is possible for me to reduce the number of parameters to 4). I do have a simulator and a criteria for me is speed in number of trials because simulation resets are costly (take 10-15 seconds on average).
One of the best local search algorithms for low number of dimensions (up to about 10 or so) is the Nelder-Mead simplex method. By the way, it is used as the default optimizer in MATLAB's fminsearch function. I personally used this method for finding parameters of some textbook 2nd or 3rd degree dynamic system (though very simple one).
Other option are the already mentioned evolutionary strategies. Currently the best one is the Covariance Matrix Adaption ES, or CMA-ES. There are variations to this algorithm, e.g. BI-POP CMA-ES etc. that are probably better than the vanilla version.
You just have to try what works best for you.
In addition to evolutionary algorithm, I recommend you also check reinforcement learning.
The right method depends a lot on the details of your problem. How many parameters? Do you have a simulator? Do you work in simulation only, or also with real hardware? Speed is in number of trials, or CPU time?
I work at an airport where we need to determine the visibility conditions of pilots.
To do this, we have signs placed every 200 meters along the runway that allow us to determine how far the visibility is. We have multiple runways, and the visibility needs to be checked every hour.
Right now the visibility check is done manually with a human being who looks at the photos from the cameras placed at the end of each runway. So it can be tedious.
I'm a programmer who has very little experience with machine learning, but this sounds like an easy problem to automate. How should I approach this problem? Which algorithms should I study? Would OpenCV help me?
Thanks!
I think this can be automated using computer vision techniques. openCV could make the implementation easier. If all the signs are similar then ,we can train our program to recognize the sign in a specific conditions(lights). Then, we can use the trained classifier to check for the visibility of signs every hours using a simple script.
There is harr-like feature extraction already in openCV. You can use to train classifier which will output a .xml file and use that .xml file for detecting the sign regularly.
I have done a similar project RTVTR(Real Time Vehicle Tracking and Recognition) using openCV and it worked great. http://www.youtube.com/watch?v=xJwBT76VEZ4
Answering to your questions:
How should I approach this problem?
It depends on the result you want/need to obtain. Is this an "hobby" project (even if job-related) or do you need to build a machine vision system to solve the problem and should it be compliant with some regulations or standard?
Which algorithms should I study?
I am very interested in your question but I am not an expert in the field of meteorology and so searching in the relative literature is, for me, a time consuming task... so I reserve to update this part of the answer in the future. I think there will be different algorithms involved in the solution of the problem, some are very general like for example algorithms for the image segmentation, some are very specific like for example how to measure the visibility.
Update: one of the keyword for searching in the literature is Meteorological Visibility, for example
HAUTIERE, Nicolas, et al. Automatic fog detection and estimation of visibility distance through use of an onboard camera. Machine Vision and Applications, 2006, 17.1: 8-20.
LENOR, Stephan, et al. An Improved Model for Estimating the Meteorological Visibility from a Road Surface Luminance Curve. In: Pattern Recognition. Springer Berlin Heidelberg, 2013. p. 184-193.
Would OpenCV help me?
Yes, I think OpenCV can help giving you a starting point.
An idea for a naïve algorithm:
Segment the image in order to get the pixel regions belonging to the signs and to the background.
Compute the measure of visibility according to some procedure, the measure is computed by a function that has as input the regions of all the signs and the background region.
The segmentation can be simplified a lot if the signs are always in the same fixed and known position inside the image.
The measure of visibility is obviously the core of the algorithm and it can be performed in a lot of ways...
You can follow a simple approach where you compute the visibility with a mathematical formula based on the average gray level of the signs and background regions.
You can follow a more sophisticated and machine-learning oriented approach where you implement an algorithm that mimics your current human being based procedure. In this case your problem can be framed as a supervised learning task: you have a set of training examples, each training example is a pair composed by a) the photo of the runway (the input) and b) the visibility related to that photo and computed by human (the desired output). Then the system is trained by means of the training set and when you give a new photo as input it will give you back the visibility measure. I think you have a log for past visibility measures (METAR?) and if you saved the related images too, you will already have a relevant amount of data in order to build a training set and a test set.
Update in the age of Convolutional Neural Networks:
YOU, Yang, et al. Relative CNN-RNN: Learning Relative Atmospheric Visibility from Images. IEEE Transactions on Image Processing, 2018.
Both Tensor and uvts_cvs 's replies are very helpful. While the opencv mainly aims to recognize the sign pattern or even segment it from the background, when you extract the core feature in your problem : visibility, you may still need to include the background signal in your training set. I assume manual check of visibility is based on image contrast, if so, the signal-to-noise ratio(SNR) or contrast-to-noise ratio(CNR) is a good feature in learning. A threshold is defined to classify 'visible-1' and 'invisible-0'. The SNR/CNR can be obtained automatically especially if your sign position and size are fixed in your camera images.
Gather whole bunch of photos and videos and propose it as a challenge on Kaggle. I am sure many people would like to try solve it, even if reward would not be very high.
You can use the template matching functionality of openCV:
http://docs.opencv.org/doc/tutorials/imgproc/histograms/template_matching/template_matching.html
Where the template is the sign. If you manage to find a correct match, then the sign is visible. I think you can also get a sense of the scale of the sign in the image from that code.
As this is a very controlled and static environment, you have perfect conditions to estimate the visibility with vision-based approaches. Nonetheless, it is not so easy to decide which approach to take. In my thesis, I am reviewing this topic in depth for the less well-controlled environment of road traffic. See: LENOR, Stephan. Model-Based Estimation of Meteorological Visibility in the Context of Automotive Camera Systems. 2016. Doktorarbeit. (https://archiv.ub.uni-heidelberg.de/volltextserver/20855/1/20160509_lenor_thesis_final_print.pdf).
I see two major directions you could follow up:
Model-based approaches: Advantages: Not so much dependent on your very specific setup. You do not need heavy collection of data.
Data-based approaches/ML: Advantages: Can hide the whole complexity of different light and weather conditions. You seem to have a good source of data if there are people doing the job right now. Very promising without much engineering effort (just use a light-weighted CNN with few layers or so).
You could also combine both, etc. etc. If you are still interested in a solution, you can contact me again and I am happy to consult in more depth.
I want to do pedestrian detection and tracking.
Input: Video Stream from CCTV camera.
Output:
#(no of) people going from left to right
# people going from right to left
# No. of people in the middle
What have i done so far:
For pedestrian detection I am using HOG and SVM. The detection is decent with high false positive rate. And its very slow as i am running in android platform.
Question:
After detection how to do I calculate the required values listed above. Can anyone tell me what is the tracking algorithm I have to use and any good algorithm for pedestrian detection.
Or should I use tracking algorithm? Is there a way to do without it?
Any references to codes/blogs/technical papers is appreciated.
Platform: C++ & OpenCV / android.
--Thanks
This is somehow close to a research problem.
You may want to have a look to this website which gathers a lot of references.
In particular, the work done by the group from Oxford present therein is pretty close to what you are doing, since their are using HOG for detection. (That work has been extremely illuminating for me).
EPFL and Julich have as well work done in the field.
You may also want to give a look to this review which describes several detection/tracking techniques, often involving variants of the HOG algorithm.
Along with #Acorbe response, I suggest the publications section of this (archived) website.
A recent work at the end of last year also released a code base here:
https://bitbucket.org/rodrigob/doppia
There have also been earlier pedestrian detector works that have released code as well:
https://sites.google.com/site/wujx2001/home/c4
http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians
The best accurate way is to use tracking algorithm instead of statistic appearance counting of incoming people and detection occurred left right and middle..
You can use extended statistical models.. That produce how many inputs producing one of the outputs and back validate from output detection the input.
My experience is that tracking leads to better results than approach above. But is also little bit complicated. We talk about multi target tracking when the critical is match detection with tracked model which should be update based on detection. If tracking is matched with wrong model. The problems are there.
Here on youtube I developed some multi target tracker by simple LBP people detector, but multi model and kalman filter for tracking. Both capabilities are available in opencv. You need to when something is detected create new kalman filter for each object and update in case you match same detection. Predict in case detection is not here in frame and also remove the Kalman i it is not necessary to track any more.
1 Detect
2 Match detections with kalmans, hungarian algorithm and l2 norm. (for example)
3 Lot of work. Decide if kalman shoudl be established, remove, update, or results is not detected and should be predicted. This is lot of work here.
Pure statistic approach is less accurate, second one is for experience people at least one moth of coding and 3 month of tuning.. If you need to be faster and your resources are quite limited. You can by smart statistic achieve your results by pure detection much faster and little bit less accurate. People are judge the image and video tracking even multi target tracking is capable to beat human. Try to count and register each person in video and count exits point. You are not able to do this in some number of people. It is really repents on, what you want, application, customer you have, and results you show to customers. If this is 4 numbers income, left, right, middle and your error is 20 percent is still much more than one bored small paid guard should achieved by all day long counting..
https://www.youtube.com/watch?v=d-RCKfVjFI4
You can find on my BLOG Some dataset for people detection and car detection on my blog same as script for learning ideas, tutorials and tracking examples..
Opencv blog tutorials code and ideas
You can use KLT for this purpose as this will tell you the flow of person traveling from left to right then you can compute that by computing line length which in given example is drawn using cv2.line you can use input parameters of this functions to compute your case, little math involved. if there is a flow of pixels from left to right this is case 1 or right to left then case 3 and for no flow case 2. Or you can use this basic tutorial to track object movement. LINK
A general question: is it possible to retrieve information about how far away e.g. a computer is from a wifi-router. For instance I want to get data on my computer if I'm 10 meters away from my home-wifispot or 2 meters.
Any idea if that is even possible?
Edit: How about bluetooth? Is it possible to get information about how far away bluetooth-connected devices are one from another?
I would recommend a measuring line or just good-old-fashioned guesstimating.
There is no "simple" way to do it (complex ways may involve building "accurate" signal maps ahead of time or trying to fit a better equation which is still subject to anumber of the limitations with the naive rule) and the rule of thumb "1/r^2" is just that -- a general rule of thumb. On the other hand, perhaps there is some existing software that will show you your RSS strength and make the task feel accomplished :-)
You will find useful links if you google for "RSS signal distance". This kind of task seems quite a common topic in academia w.r.t. small wireless devices ("motes") as well and there have been some interesting approaches to this problem such as the fitting of secondary low-frequency acoustic sensors.
You can query the signal strength which is some kind of indication of distance and obstructions and a few other factors all rolled into one measure. With just plain wifi though this isn't possible directly.
Try measuring the response time of the router to pings, with the data rate set to constant to avoid that effecting the response time. Take lots of samples and remove outliers to reduce errors, but you will still have a substantial quantization error. Subtract the latency of the router and computer, divide by 6 then multiply by the speed of light and hopefully you will have the distance to a resolution of a few metres.