I want to implement Monte Carlo Localization in a project I'm doing. The first thing I did is I tried to implement it in a virtual robot navigating a 2D world. Right now the program is really slow, doesn't work, and I'm making little progress. So I'm wondering if anyone can point to an example of Monte Carlo Localization I can look at.
Take a look at the AMCL driver that is part of ROS. That implements the Adaptive Monte Carlo Localization algorithm described in Probabilistic Robotics.
If you have any questions specific to that driver and how it works, the ROS community has a Q&A site at ROS Answers. That would be a good forum for questions specific to that implementation.
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Are there any known approaches of making a machine learn calculus?
I've learnt that it is quite simple to teach calculating derivatives because it is possible to implement an algorithm.
Meanwhile, an implementation of integration is possible but is rarely or never fully implemented due to the algorithmic complexity.
I am curious whether there are any academic successes in the field of using machine learning science to evaluate and calculate integrals.
Edit
I am interested in teaching a computer to integrate using neural networks or similar methods.
My personal opinion it is not possible to feed into NN enough rules for integrating. Why? Because NN are good for linear regression ( AKA approximation ) or logical regression ( AKA classification ). Integration is neither of them. It is calculation task according to some strict algorithms. So from this prospective it's good idea to use some mathematical ways to integrate.
Update on 2020-10-23
Right now I'm in position of being ashamed by new developments according to news. Facebook recently announced that they developed some kind of AI, which is good in solving integrations.
There quite a few number of maths software that will compute derivatives and integral calculus for you. Some of the popular software include MATLAB, Maple, Mathematica, etc. These software will help you learn quite easily.
As for you making a machine learn calculus ...
You can read up on the following on wikipedia or other books,
Newton's Method - Solve the roots of a function numerically
Monte Carlo Integration - uses RNG to compute numeric integration
Runge Kutta Method - Solves ODE's iteratively
There are many more. These are just the ones I was taught in undergraduate school. They are also fairly simple to understand, depending on your level of academia. But in general, people have been try to numerically compute solutions to models since Newton. Computers have just made everything a lot easier.
For some time, I have been using OpenCV. It satisfied all my needs of feature extraction, matching and clustering(k-means till now) and classification(SVM). Recently, I came across Apache Mahout. But, most of the algorithms for machine learning are already available in OpenCV as well. Are there any advantages of using Mahout over OpenCV if the work relates to Videos and Images ?
This question might be put on hold since it is opinion based. I still want to add a basic comparison.
OpenCV is capable of anything about vision and ml that is possibly researched, or invented. The vision literature is based on it, and it develops according to the literature. Even the newborn ml algorithms -like TLD, originated on MATLAB- (http://www.tldvision.com/) can also be implemented using OpenCV (http://gnebehay.github.io/OpenTLD/) with some effort.
Mahout is capable, too and specific to ml. It includes not only the well known ml algorithms, but also the specific ones. Say you came across to a paper "Processing Apples with K-means Orientation Filtering". You can find OpenCV implementations of this paper all around the web. Even the actual algorithm might be open source and developed using OpenCV. With OpenCV, say it takes 500 lines of code, but with Mahout, the paper might be already implemented with a single method making everything easier
An example about this case is http://en.wikipedia.org/wiki/Canopy_clustering_algorithm, which is harder to implement using OpenCV right now.
Since you are going to work with image data sets you will need to learn about HIPI, too.
To sum up, here is a simple pro-con table:
know-how (learning curve): OpenCV is easier, since you already know about it. Mahout+HIPI will take more time.
examples: Literature + vision community commonly use OpenCV. Open source algorithms are mostly created with C++ api of OpenCV.
ml algorithms: Mahout is only about ml, whereas OpenCV is more generic. Still OpenCV has access to basic ml algorithms.
development: Mahout is easier to work with in terms of coding and time complexity (I am not sure about the latter, but I reckon it is).
I'm currently working on a project that aims to recognize objects in an indoor household type of environment and to roughly represent the location of these objects on a map. It is hoped that this can all be done using a single Kinect camera.
So far I have managed to implements a basic object recognition system using OpenCV's SURF library. I have followed and used similar techniques to what has been described in "OpenCV 2 Computer Vision Application Programming Cookbook".
I'm now slowly shifting focus to the mapping portion of this project. I have looked into RGBDSLAM as a method to create 3D maps and represent any objects found. However, I can't seem to find a way to do this. I have already asked a question about this at http://answers.ros.org/question/37595/create-semantic-maps-using-rgbdslam-octomaps/ with no luck so far.
I have also briefly researched GMapping and MonoSLAM but I'm finding it difficult to assess whether these are suitable since I've only just started learning about SLAM.
So, any advise on these SLAM techniques would be much appreciated!
I'm also open to alternatives to what I've talked about. If you know of any other methods of creating semantic maps of environment then please feel free to share!
Cheers.
I have used A.J Davison's MonoSLAm method and it is only suitable for small environments like a desktop or a small room (using a fish eye lens). Try to use PTAMM (by Dr. Robert Castle), its much more robust and the source code is free for academic use.
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What should someone study at university level if he/she wants to get into robotics and build robotics? So far 'Mechatronics' seems to be the field I'm looking for? I looked at a few plain 'robotics' courses but they seem to be only about the electrical and computer work, and don't include any details on building the mechanical components of robots?
I'm a professional robotics research consultant, with 30 years of experience working for organizations like SRI International and JPL.
Like computers, robotics has quite a strong divide between the software and the hardware. Hardware is further subdivided into actuators and sensors.
If you'd said "I want to get into computers", I would explain that only a few hardware engineers actually design and build physical computers--most researchers assume that the hardware and firmware has been built already, and then they worry about the software--how to make the system actually work.
Similarly with robots, building the hardware is a job for the mechanical engineers (to design the structure and heat dissipation), with little bits and pieces for power electrical engineers (to spec the motors) and computer engineers (to design the firmware silicon). Next-generation robots also use industrial designers (to make the outsides look pretty, and the insides fit well together).
Research areas for actuator design include fingered hands; tentacles; hummingbird and other bird and insect wings; springy wheels; legs; non-electronic designs for high radiation areas; and surgical instruments.
With cameras in every cell phone, vision sensors are mostly a solved problem at this point. Research areas for sensor design include smart flexible tactile skin, brain wave sensors, and other biomedical sensors. There's still some room for good force sensors as well. These fall in the realms of materials engineering, computer engineering, mechanical engineering, and biomedical engineering.
In order to drive the actuators properly so they don't shake themselves apart, you need a control-theory engineer. Start with Fourier transforms so that you can then understand z-transforms. The learning curve on this mathematics is extremely steep, and careers are quite few, so either you have to be born to be a controls engineer or you should let someone else handle these lower-level details for you.
Signal processing, for the medium- and low-level sensor drivers, has been under the domain of the EEs historically. This works its way up to image processing, which falls under computer science, and then image understanding, which is in the A.I. branch of CS.
However, as I mentioned, the hardware, firmware, and drivers are all manufacturing details that you solve once and then sell forever. Anybody can buy a Lego or a Bioloids kit off the shelf now, and start working with motors. It's not like 2006, when the Fujitsu HOAP humanoid robot we were working with at JPL was a $50,000 custom-ordered special.
Most of what I consider the really interesting work starts by assuming the hardware and drivers have already been accomplished--and then, what do you do with the system? This is completely in the realm of software.
Robotic software control starts with 3D simulators, which in turn are based on forward kinematics; eventually inverse kinematics; dynamics, if you feel like it; and physics-engine simulations. Math here centers around locations [position + orientation], which are best represented by using [4x4] homogeneous coordinate transformation matrices. These are not very hard, and you can get a good background in them from any computer graphics textbook. Make sure you follow the religion of post-multiplying by matrices ending in a column vector on the right; this allows you to chain base-to-waist-to-shoulder-to-elbow-to-hand kinematics in a way that you'll be able to understand. Early textbooks proposed premultiplying using row vectors, because they thought it wouldn't make a difference. It does.
Of course the physics engines require a decent knowledge of physics.
Higher-level processing is accomplished using artificial intelligence, usually rules-based systems. Natural-language processing also can tie in linguistics and phonetics. Speech recognition and speech generation are again mostly signal processing, taught in EE and CS.
Recent advances work on Big Data, which uses statistics, Bayesian reasoning, and bases vector spaces (from mathematics).
Robotics has not yet broken out. It is still at the level cell phones were at when Gordon Gecko was walking on the beach talking into a "portable phone" the size of a shoe. I don't see robots becoming ubiquitous before 2020. Around 2025, being a robot programmer will be in demand as much as being an app programmer is today. Study lots of A.I. Start early.
State-of-the-art humanoid robot system design as of 2006 [short movie]:
http://www.seqcon.com/caseJPL.html
Very high level block diagram of components [graphic]:
http://www.seqcon.com/images/SystemSchematic640.gif
I would highly recommend looking into Artificial Intelligence for Robotics on Udacity, it is very interesting course that covers the software and AI part. Also Coursera offers a free online robotics course, and other courses as well that are very relevant and useful to Robotics.
Mechanical and electrical engineering and computer science.
Mechanical engineering will inform choices about servos, linkages, gears, and all other mechanical components.
Control theory is the junction of mechanical and electrical engineering. You'll need that.
So much of control is digital these days, so EE and computer science will be a part of it.
It's a big field. Good luck.
Industrial robotics is usually handeled by Mechanical Engineers, and sport/team robotics by electical engr, electronics engr, or computer science majors. It all depends on what you mean by "robotics". Also, in case nobody else mentions it, a Masters degree is strongly encouranged.
As an added bonus the math used in industrial robotics, is directly linked to math for game development. There isn't really a clear cut line of who is supposed to be doing what in robotics.
Mechtronics is the current field of study for those interested in robotics. It combines mechanical, electrical, controls, and software as relates to robotics.
In the past we came from many different backgrounds, mechanical engineers, electrical, electronics, and software. I am an Application Engineer for robot manufacturer. I started out in Avionics, moved to automated test equipment, then to automated material delivery systems, I became a robotics service technician and manager then moved over to application programing and training.
One final note, be prepared to keep learning. This is a field that is constantly changing and evolving.
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I'm looking for a book similar to "Introduction to Algorithms" by Thomas Cormen geared towards DSP algorithms. Is there anything as thorough as Cormen on the DSP market?
EDIT
I should say I'm looking for a book that's analogous to The Joy of Cooking.
Disclaimer - I am not familiar with the Cormen book so I'm not quite sure what you're looking for.
I'm a huge fan of "A Digital Signal Processing Primer" by Ken Steiglitz. It introduces DSP concepts like sampling, as well as simple filtering implementations, without relying just on math for explanation. Cookbook equivalent: You know how to boil water on a stove, but you're nervous about the rest.
A more advanced book, more of a practitioner's handbook than a text, is "Theory and Application of Digital Signal Processing" by Lawrence Rabiner and Bernard Gold. Their explanation of the overlap-save FFT technique for convolution, in particular, is the best I've ever come across. Cookbook equivalent: Maybe Joy of Cooking, maybe the Cordon Bleu tome.
And "Telecommunications Breakdown" by Richard Johnson and William Sethares is great for taking some DSP concepts and bringing them to life by implementing a radio in software. Cookbook equivalent: A tour through a specific cuisine, and explains what "braising" is along the way.
Hope these are of use to you!
For theory, I like Understanding DSP by Rick Lyons, which also has some nice "recipe-type nuggets".
More practical, and much more "nuggetty" is Streamlining DSP, same author. There's some really interesting stuff in there (IMHO!). Some of it is of the "lost knowledge" variety - especially in these days of just running Matlab's filter design functions. Some of it relates to limited hardware machines (which is great for tiny microcontroller or FPGA implementations).
The articles are written by serious, practicing DSP engineers (many of whom hang out on news:comp.dsp) in a very accessible style.
(I'm afraid I'm no good with cooking analogies though :)
Just for the record and benefit to others, I would recommend The Scientist and Engineer's Guide to Digital Signal Processing.
This is a good book for beginners.
There are a few online books available as well at the great DSPRelated.com:
INTRODUCTION TO DIGITAL FILTERS WITH AUDIO APPLICATIONS by JULIUS O. SMITH
MATHEMATICS OF THE DISCRETE FOURIER TRANSFORM (DFT) WITH AUDIO APPLICATIONS by JULIUS O. SMITH
This is not a book but I'm sure it'll be a valuable resource: The Ecole Polythechnique de Lausanne is starting a free online course on digital signal processing on February 18th 2013: https://www.coursera.org/course/dsp.
Also, the guys teaching it co-authored a book on the topic: http://www.sp4comm.org/
A second vote for the Rick Lyons book. You might also want to get a couple of DSP "bibles", e.g. Oppenheim & Schafer and Proakis & Manolakis, which are more theoretical but cover more ground.
The DSP handbook: algorithms, applications and design techniques - Bateman, Andrew, Paterson-Stephens, Iain 2002
and
Introduction to digital signal processing - Meddins, Bob 2000
Have basically made my ADSP module a breeze (so far). They are also at the top of the suggested reading list. As such, both are fairly beginner friendly, and the latter includes Matlab examples.
The former is probably more Delia, while the latter is more 'my first cook book'.
I will also add Vetterli, Kovacevic, and Goya's Foundations of Signal Processing, which can be downloaded for free.