Tag Archives: Kinect

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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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.

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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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

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.