Website Redesign and Update

After a lengthy hiatus following the manic end of my University degree (for which I got a First Class Master’s Degree) I have decided to come back to the website to redesign it and, most importantly, update the content.

I have spent the last few weeks redesigning it to make it better looking and more slick. I have updated the three static pages (home, programming and design), but have not yet updated the blog – hence why it is currently a completely different style.

I have plans for the blog and will update it in the near future but this requires, at the very least, creating a custom WordPress skin to match the other pages. My preferred approach is actually to try to move away from WordPress altogether either to a custom solution of my own or a much more cut-down system. Obviously this will take me some time to do and settle on one of the various options so I wanted to make the three new pages live ASAP rather than hold them back. This allows me to create new content while I work on the new blog design.

I intend to create some final blog posts about my Individual Project (which I completed in May and haven’t posted about since February) as well as post about some of the other programming and design projects I have done.
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Reconfiguring the Kinect

Having finished restructuring my code I decided to try the whole unit out, only to find that I had been developing my code using a Kinect for Windows, however the Kinect that had been soldered to the Roomba is a Kinect for XBox.

Thankfully the OpenNI drivers support both (if anything the Kinect for XBox is better supported), however I had some trouble making it work with my code (or indeed the provided samples). After much searching I replaced the camera configuration XML file that I had been using. Another buycbdproducts I had not realised is that the Kinect has two powered-on modes, one – a standby mode – where the front green light flashes but it does not respond to queries, and another where it is fully on. As far as I can tell these modes are indistinguishable. The way that the Kinect is wired to the Roomba means that when the Roomba is charging only enough power for the Kinect to enter the first standby mode is provided.

Once I had figured this out and recompiled with the new configuration XML the camera worked perfectly and was identical to the Kinect for Windows one. The results are shown below:

The first depth view (coloured) from the Kinect mounted on the Roomba.

The first depth view (coloured) from the Kinect mounted on the Roomba.

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Controlling the iRobot Create

I have spent the week working on accessing the iRobot Create’s interface and integrating this functionality with the rest of my code.

The iRobot Create is controllable via the Create Open Interface, a specification for which can be found here. This is simply a series of commands that can be sent via the serial connection to the device. In my opinion this is a great way of doing it and avoids the hassle of configuring and learning a complicated library. It does however mean that I had to create code myself to do the functions that I wanted (e.g. driving the motors and reading off distance measurements), something I first prototyped in Python to check it worked before moving to C++ so it could be integrated with the rest of my project.

This gave me a good chance to restructure my code from a very arrangement where I had all the Pandaboard’s code in a single, messy file, and all the code for the host computer in another file, with a fair amount of duplicated code. I restructured the code to split the components into different files for: the computer vision, the serial interface, the networking interface and the robotic controls. I then created a separate, much reduced, file to be run on the host computer which makes use of the same networking components. The compilation is then controlled by a single Makefile which will build the correct versions for each machine.

The new structure took me several days to implement and iron out the bugs, but it is significantly better and more object-oriented approach than I used previously and will definitely save me time in the long run.
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Wireless Setup on the Pandaboard

Having given the Pandaboard a battery pack the only wire that remained connected to it was the Ethernet cable. I had hoped that simply switching to using WiFi, something that the Pandaboard has built-in, would be an easy task, however I could not have been more wrong.

Firstly it took me almost a day to get the WiFi working on the Pandaboard, I tried a large number of guides all asking you to install and do different things but to no avail. The guide I finally used which worked was the one available here, for which there is a video available via a link at the top of the section. A useful resource for configuring the /etc/network/interfaces file (which I sadly found after working it out myself) can be found here. I should also point out that I have to perform an additional restart of the network interface via the command:

sudo /etc/init.d/networking restart

I’m not sure why this is, however I had spent far longer than I wanted to setting it up so I decided to just accept it and added the line to the end of the /etc/rc.local file which is automatically run on start up.

I then needed to install git on my laptop, which had been running Ubuntu 10.11, however as it has been discontinued the repositories were unavailable. I therefore decided to upgrade to Ubuntu 12.10, though after many hours of research I found that my Laptop’s WiFi card (a Broadcom BCM4311) was unsupported and none of the numerous fixes listed worked. I then downgraded to 12.04 but had the same problem (again after many hours messing around). I worked my way back through the versions until I eventually settled on Ubuntu 10.04 (a Long Term Support release), which worked. This whole process took over 2 days.

A final caveat is that Ubuntu 10.04 does not support Ad Hoc networks secured with anything other than WEP, something that I was unable to get the Pandaboard to connect to after several attempts. My solution was to use an old smartphone as a Wireless Hotspot (secured with WPA2) and connect to it from both the laptop and Pandaboard. As this assigns different addresses each time you connect to it so I wrote a short script to run on the Pandaboard each time it boots up to locate the laptop and save its address. I also synchronise the system clock with the Laptop’s as, since I disconnect the battery pack when not in use, it does not stay up to date.

I now have everything working, and I intend to have as little to do with Linux’s wireless networking in the future as possible.

Powering the Pandaboard using Batteries

As my project uses the Pandaboard as a controller for a robotic system it is necessary that it be fully battery-powered. The Kinect is supplied with power directly from the Roomba’s battery, which, with the iRobot Create, is easily accessible via an output in the cargo bay which can provide 12V battery power.

The Pandaboard however runs on a 5V supply and, as it’s not designed to be run on batteries, can take very high current peaks (e.g. when the CPU is under heavy load). The Roomba’s output does not supply the required current for this, and would likely not supply a stable enough supply either.

I therefore needed a separate battery for the Pandaboard, for which I bought a 10,000 mAh, 5V 3A  USB Battery Pack (designed for charging phones when away from a power source) for £26 from Amazon. As a standard USB port cannot supply the required current it was also necessary to buy a USB-A to mini USB-B Y-Cable (designed for supplying power to external hard drives) for £2 from Amazon. This allows the power to be drawn from both the ports the battery has, up to a maximum of 3A (according to the battery’s specification), well in excess of what the Pandaboard will need.

The Pandaboard with external battery supply, connected via a Y-Cable.

The Pandaboard with external battery supply, connected via a Y-Cable.

The resulting setup can be seen in the picture above. I have not tested the full life of the battery, but it has lasted 5 hours without going below 50%.
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Installing OpenCV on the Pandaboard and JPEG Compression

Just a quick update this week as I am snowed under with other work, but in a spare 5 hours I installed OpenCV on the Pandaboard – a relatively painless experience as I am using Ubuntu Desktop 12.04 which meant I was able to pretty much just follow the instructions for that: just building it for ARM rather than x86.

Prior to this all the image processing had actually been happening on the host PC, via the raw images it received via the Ethernet cable. Obviously this was just a prototype, this would never have been a workable solution for my project. Once I had OpenCV installed on the Pandaboard I was able to rewrite my code to move the image processing over to it, and make the host PC merely a display that received and displayed the images.
I also took this opportunity to apply JPEG compression to the images before sending them, and uncompressing them for display at the other end using OpenCV’s imencode and imdecode functions. This reduced the network bandwidth from 12MB/s (sending raw images) to 2MB/s (with maximum quality, with considerable further reduction the more you reduce the quality), while increasing the FPS from 20 to 26. This will allow me to send the images over WiFi (once I have it set up).

Depth Colouring

As it’s exam season I have not been able to get as much project work done as I would like recently, however in a brief break between revision sessions yesterday I made a quick change to the way I draw the depth images (and video).

It has been bugging me for a while that the depth images come in with values in the rough range 0-6000, however my screen display program was converting them to a number in the range 0-255, thereby losing a vast amount of the potential detail. Increasing the colour depth of the grayscale image wouldn’t help overly either as it would make it no easier to visually discern the difference between values. I therefore fixed the problem by mapping it to a colour spectrum (inspired by the Hue wheel on colour pickers) rather than a grayscale one. This increases the range of values which I can display from 0-255 to 0-1530 – a six times improvement! I chose to continue mapping errors to black.

A comparison between the old, grayscale depth display (left) and the new, colour spectrum depth display (right).

A comparison between the old, grayscale depth display (left) and the new, colour spectrum depth display (right). Click for full size image.

Personally I don’t think the human eye can necessarily pick up enough information to be able to exploit the full six-times increase in the range of displayable values, however it is definitely an improvement. For example the folds in my clothing (particularly my jumper) are far more noticeable in the right hand image and it’s more obvious my arm is held in front of my body rather than parallel to it. Likewise the corner of the room is more pronounced where before it was just a light grey haze. While not a critical item for my project, it is a nice visual improvement that will make it both easier to track down bugs and more appealing to people I show it to.

When I next get an hour free the next small change I plan to make is adding compression to the video stream. Currently it streams at 12 MB/s and in the 10 minutes I was testing the coloured video, a total of 7 GB of images were streamed. This is not really practical, especially if I plan to use it over the University’s WiFi network. If I find the job too difficult to do in an hour I will give up as, once again, it is not a critical requirement.

Fixing Depth Noise from the Kinect

Last post I got the depth data streaming from the Kinect to a connected computer. Since doing that I immediately noticed that the depth data from the Kinect comes back extremely noisy (I will endeavour to upload a video to demonstrate my point in the near future). Not only are the edges of objects ‘lumpy’ rather than smooth (a result of the Kinect’s sensing method), there are depth errors constantly appearing and disappearing from frame to frame. These depth errors are all returned from the camera as having a depth of 0 (in my images these are black areas).

Depth frame capture of my room from the Kinect.

Depth frame capture of my room from the Kinect.

In this old image I have reused you can see several black regions on the image. Some of these, the larger groups, will be stable from frame to frame – for example in the above image, the fireplace under the mirror will be constantly causing errors in the depth measurements. I have yet to find an explanation for this.

There is also a large amount of noise, the smaller, patchier black regions will be noise that will randomly come and go from frame to frame. This is very annoying and far from ideal from an image processing point of view, however it is also something that should be easily fixed in software.

I tried several methods to remedy the problem. The first and simplest method I tried was just to not update pixels if their new value was 0. This was exceptionally cheap and worked surprisingly well, although it did produce some tearing on moving objects as their ‘shadow’ would be incorrectly filled in with their depth when moving in certain directions. I then tried using a weighted average method, in the hope of removing the high frequency noise (which usually lasts no more than a frame or two) while keeping the shadows cast by objects. This worked fairly well, although it was far less effective at removing the noise than the previous method, some still came through and a flickering effect could still be seen, it was just subdued. Additionally a noticeable lag could be observed on moving objects, leaving a ‘ghost trail’ behind them. Finally I tried a neighbourhood analysis method: replacing zero-value pixels with the median of the non-zero pixels in a neighbourhood around it (or not if the neighbourhood contained only zero-value pixels). This was exceptionally expensive (reducing the frame rate to just 1 or 2) and, while it did better than the weighted average approach without producing lag it also left a halo surrounding the shadows.

For the time being I will use the first and most simple method I tried, not updating pixels if their new value is 0. While this has significant problems and introduces actual artefacts into the stream (in place of the shadows) which none of the other methods did, it is extremely effective at removing the noise and is the cheapest method by far. I may look into improving the weighted average approach at a later date as I still believe it has potential.

Website Update

I spent yesterday evening updating the website, most notably the programming and design projects I’m working on / have worked on. I will endeavour to make a blog post about as many as possible (particularly the design projects) when I get the time to provide more details and, most importantly, pictures.

Streaming Video from the Kinect

Since my previous post I have been working on capturing live video from the Kinect to see what I will be working with. This will be useful later on in the project from a debugging point of view so that I can work out what the system is doing. Unfortunately it is not as simple as it might seem since I run the Pandaboard headless – thus it has no monitor to display the video stream on.

The simplest, and most obvious, solution would be to connect a monitor to the Pandaboard (it has 2 HDMI ports), however, as I intend to connect this system up to the robotic base in the near future and have it moving around, this is would be far from a long-term solution.

I therefore took the decision to stream the video frames over the network from the Pandaboard to a ‘host’ computer (a common name for the computer with which a Pandaboard communicates, although in this case a somewhat misleading one as it is not actually controlling the board in any way, merely receiving its data from it). I do this using C’s TCP/IP socket interface where the Pandaboard acts as the client while the host computer acts as the server. This is a somewhat backward way around, really the Pandaboard (the one sending the data) should be the server; I originally had a good reason for the orientation however the restrictions that forced me to do it have since been removed so this could be rewritten. Once connected, the Pandaboard sends each raw depth frame (a 640×480 array of 16-bit depth readings) over the network to the host computer.

I have also implemented the streaming protocol for the RGB data.

This network streaming does produce some overhead, reducing the frame rate by about 11 FPS for each stream running, so streaming just depth or RGB data reduces the frame rate from around 30 to 19 FPS, streaming both reduce it further to around 8 FPS. I consider this cost acceptable as the functionality will only ever be used for debugging. I could reduce the amount of data sent either by compressing the data or scaling the 16-bit values down to 8-bit values (something that is done on the host side before displaying them anyway) prior to transmission. Another possible extension is to switch to using a more standard video streaming format which, while not necessary now, would allow the video to be streamed to a web interface at a later date. This is a bridge I will cross when I come to it.
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