Second Attempt at Receiving Data from the Kinect

I have now fixed the issues I mentioned in the previous post. It turns out the problem was due to an obscure but simple-to-fix problem in my code, although I first tested the drivers on a number of other machines to rule out driver issues – which all in all took the best part of a day.

Regardless, I now have the Pandaboard interfacing with the Kinect properly and outputting the following images (currently at 30 FPS, the maximum rate the Kinect’s depth-camera supports):

Depth frame capture of my room from the Kinect.

Depth frame capture of my room from the Kinect.

Colour frame capture of my room from the Kinect.

Colour frame capture of my room from the Kinect.

The depth image’s gradient is scaled to the maximum depth to convey the most information possible. In this image the maximum depth is comparatively far due to the mirror slightly throwing the Kinect so that the mirror’s depth is the depth of the points it is reflecting. This causes it to appear further away than it really is, in turn causing the gradient to be accordingly scaled, thus losing some of the information in the foreground.
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First Attempt at Receiving Data from the Kinect

Having set up the Pandaboard with the Kinect as mentioned previously I have been experimenting with the OpenNI Library. I have tried simply extracting the depth data from the Kinect using a technique developed from the samples. This makes use of a DepthGenerator and a DepthMetaData container to extract the information, however so far my attempts at getting meaningful data have fallen somewhat flat.

The best depth image I have generated so far (from many less successful attempts) is shown below. This is plotted by scaling all the depth values by the maximum in order to give a full colour range of black to white.

First attempt at extracting depth data.

While this is clearly not ideal, and I am currently in the process of doing more research and experimenting to try to work out what I am doing wrong, it is definitely progress.

Setting up Ubuntu and Kinect Drivers on the PandaBoard

Having now had to install and configure Ubuntu on the PandaBoard three times I thought I would make a blog post about the setup procedure. If nothing else this will serve as a reference to myself should I have to do it again. I am using an Ubuntu 12.04 Desktop build.

 

Installing Ubuntu 12.04 Desktop on an SD card:

The first step is to install Ubuntu on an SD card. For this I followed the instructions provided at the Ubuntu Wiki, which I will briefly go through here for a Linux-based host computer (the Wiki provides instructions for doing this on other OSs). You do not need a serial cable as some guides suggest.

1. Download the Texas Instruments OMAP4 (Hard-Float) preinstalled desktop image from the official site.

2. Insert the SD card into the host computer and make a note of its device interface. You can find the device’s name using the GUI (it should appear at the top of the file explorer window when you navigate into the disk). Knowing this you can then find the associated device interface using the command:

mount -l

and finding the device’s name in the list. The device interface usually looks like /dev/sdX (where X is a single letter, ignoring any subsequent numbers). Once you have found this, unmount the disk. This can be done with the GUI by hitting the eject button.

3. Next run the following commands to unextract the image, copy it over and flush the system buffers. Make sure to replace /dev/sdX with the device interface identified in the previous step:

  1. gunzip ubuntu-12.04-preinstalled-desktop-armhf+omap4.img.gz
    sudo dd bs=4M if=ubuntu-12.04-preinstalled-desktop-armhf+omap4.img of=/dev/sdX
    sudo sync

This will take some time (around 30 minutes).

 

Configuring the installation:

Once the image is written to the SD card it can be removed from the host computer and inserted into the Pandaboard. At this stage the Pandaboard will need a 5V power supply, an ethernet cable, a monitor, a keyboard and optionally a mouse (I am making do without one).

1. Turn on the Pandaboard (by simply connecting the power cable) and it should begin booting (it takes a while for anything to appear on the display). When it has finished (it takes about 5 minutes) it will begin installing Ubuntu which takes around 50 minutes to complete.

2. Once Ubuntu has finished installing, update the system to the latest version using the commands:

sudo apt-get update
sudo apt-get upgrade

This again will take a while to complete.

3. After updating I chose to install an ssh server so that I would be able to control it from my desktop computer. This is achieved by using the command:

sudo apt-get install openssh-server

The default configuration should work fine, however it can be changed if necessary by editing the file /etc/ssh/sshd_config. From this point on everything can be performed over ssh.

 

Installing the Kinect Drivers:

Finally, to install the Kinect Drivers I followed the instructions provided by Pansenti, which are simple to follow and highly detailed so I don’t feel it necessary to repeat them here. I shall, however, point out a couple of deviations I made from their instructions:

1. The bulk-install method mentioned on Pansenti’s site did not work for me (both times I tried it the PandaBoard hung up unexpectedly half way through). Instead I had to install all the drivers separately, like so:

sudo apt-get install gcc-multilib
sudo apt-get install libusb-1.0.0-dev
sudo apt-get install git-core
sudo apt-get install build-essential
sudo apt-get install doxygen
sudo apt-get install graphviz
sudo apt-get install default-jdk
sudo apt-get install freeglut3-dev
sudo apt-get install libopencv-dev

This step takes a considerable amount of time (at least an hour and a half, more depending on your internet connection): many of the libraries listed are very large (they total nearly 1GB) and heavily compressed.

2. I did not find it necessary to alter the MAKE_ARGS to change the threading flag.

3. Once I had finished the installation the tests mentioned did not work. After some head-scratching I realised this was, as very briefly mentioned at the bottom of the article, because the installation removes a kernel module – gspca_kinect – which comes with Ubuntu 12.04 and otherwise stops the Kinect from being visible to the rest of the system. For this removal to take effect the PandaBoard has to be restarted, after which the tests will function as the guide says: allowing a default test to be run as follows:

cd ~/kinect/OpenNI/Platform/Linux/Redist/OpenNI-Bin-Dev-Linux-Arm-v1.5.4.0/Samples/Bin/Arm-Release
./Sample-NiSimpleRead

Progress Update

Over the past week or so I have acquired a Pandaboard to run the project on, and organised a Microsoft Kinect and an iRobot Create (a Roomba without the vacuum cleaner compartment) for the main components.

I have spent some time setting up the Pandaboard and installing an Operating System. I have opted to use an ARM desktop build of Ubuntu 12.04 for buycbdproducts time being as it is relatively straight forward to use and people have previously got a Pandaboard running it and interfacing with a Kinect via the open source drivers. As such I figured that while it is a fairly heavy-weight Operating System for the task in hand – especially from a performance/power-consumption point of view – it is a known working configuration and is a great place to start and learn the process of getting it working.

I have tried to install and run the drivers, however while installing one of the libraries the system crashed and eventually had to be manually restarted. This left it in a partially upgraded state which did not find my somewhat heavy-handed restoration attempt pleasing. I am now back to square one and need to reinstall Ubuntu on the SD card and try again.

Individual Project Overview

One of the key motivations in setting up this blog was so that I could document the work on my Individual Project (part of my 4th year degree program at the University of Bristol). For this I am making a low-power, autonomous litter bin. This will use a Kinect (or similar device) to identify groups of people who would potentially generate litter and move towards them; avoiding crowds or moving people. While implementing this I intend to investigate low-power machine vision solutions in general and how the Kinect might be usefully leveraged in such an application.

The fundamental aims for my project are as follows:

  • A low-power, Kinect-based, computer-vision solution.
  • Real-time, autonomous decision-making.
  • Robust, unobtrusive behaviour.
  • Human behavioural analysis to intelligently detect potential litter generation.

From a hardware point of view I intend to use a Roomba (a robotic vacuum cleaner that has an API available to control it) as the wheeled-base for the project and a PandaBoard to perform the processing.

I shall update with more detailed information in the coming weeks.

Blog Update

As I mentioned in my previous post, I wasn’t happy with my, then current, theme, Gridiculous. The browser compatibility issues were the main problem, but I also wasn’t 100% happy with the theme itself.

Since then I have learnt quite a bit more about how themes in WordPress are written and I have subsequently written my own in the form of a child theme built on top of the twentytwelve theme. This has the advantage of both being a relatively plain theme (so easy to customize) and, having been written by WordPress themselves, a pleasure to modify. Since it’s a child theme it will update as twentytwelve is updated so it should be fairly robust. I was surprised at just how painless the whole procedure was: the bulk of the rewrite took me just 6 hours with a small amount of planning before hand and a bit of messing around uploading it afterwards. I’m very happy with the end result and feel far more comfortable editing these themes now so I should be able to fix any problems as they arise; as I’m sure they will!

New Blog

Finally got round to setting up my blog. I had planned to write my own theme (which I have designed but not yet converted to WordPress format), however due to time constraints that will no longer be possible for at least several months so I have decided to use the Gridiculous theme in the mean time. Unfortunately I have been having some browser compatibility issues with it – so far the only working browser I have found is Chrome on Linux: Chrome on Windows appears to have a hard time drawing the smaller text; Firefox on Windows messes up the header and uses a different, much larger font; Internet Explorer stretches the header image and also uses a different, much larger font; Firefox on Linux messes up some of the images and no mobile browsers I’ve tested fix the background to the screen properly. The lightbox also crops random parts off the images, which it doesn’t on other themes. I may try to fix these issues myself or fall back to a better tested theme (e.g. twenty-eleven) – I’m not yet decided.
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