It was pretty cool to see RTAB-Map build a 3D map of its environment using data generated by my hands waving a Xbox 360 Kinect around. However, that isn’t very representative of rover operation. When I wave it around manually, motions are mostly pan and tilt but not much translation. The optical flow of video feed from a rover traveling along the ground would be mostly dominated by forward travel, occasional panning as the vehicle turns, and limited tilt. So the next experiment is to put the Kinect on a rover to see how it acts.
This is analogous to what we did at SGVTech when I first brought in the LIDAR from a Neato vacuum: we placed on top of SGVHAK Rover and drove it around the shop to see what it sees. Unfortunately, the SGVHAK Rover is currently in the middle of an upgrade and disassembled on a workbench. We’ll need something else to stand in for a rover chassis. Behold, the substitute rover:
Yes, that is a Xbox 360 Kinect sensor bar taped on top of an office chair. The laptop talking to the Kinect can sit on the chair easily enough, but the separate 12V power supply took a bit more work. I had two identical two-cell lithium battery packs. Wiring those two ~7.4V volt packs in series gave me ~14.8 volts, which fed into a voltage regulator bringing it down to 12V for the Kinect. The whole battery power contraption is visible in this picture taped to the laptop’s wrist rest next to the trackpad.
This gave us a wheeled platform for linear and rotational motion along the ground while keeping the Kinect at a constant height. This is more representative of the type of motion it will see mounted on a rover. Wheeling the chair around the shop, we would see the visual odometer performance is impressive, traveling in a line for about three meters resulted in only a few centimeters of error between its internal representation and reality.
We found this by turning the chair around to let the Kinect see where it came from and compare the newly plotted dots against those it plotted three meters ago. But this raised a new question: was it reasonable to expect that RTAB-Map algorithm match the new dots against the old? Using distance data to correct for odometer drift was one thing Phoebe could do in GMapping. I had hoped RTAB-Map would use new observations to correct for its own visual odometry drift. But instead, it started plotting features a few centimeters off from their original position, creating a “ghost” in point cloud data. Maybe I’m using RTAB-Map wrong somewhere… this is worrisome behavior that needs to be understood.
I brought my modified Xbox 360 Kinect and my laptop to this week’s SGVTech meetup. My goal for the evening was to show everyone what can be done with an old game console accessory and publicly available open source code. And the best place to start showcasing RTAB-Map is to go through the very first tutorial: Handheld Mapping with RGB-D sensor.
When I installed OpenKinect on my Ubuntu laptop, I was pleasantly surprised that it was offered as part of Ubuntu software repository making installation trivial. I had half expected that I would have to download the source code and struggle to compile without errors.
Getting handheld RGB-D mapping up and running under ROS using RTAB-Map turned out to be almost as easy. They’re all available on ROS software repositories, again sparing me the headache of understanding and fixing compiler errors. That is, as long as a computer already has ROS Kinetic installed, which is admittedly a bit of work.
But if someone is starting with a working installation of ROS Kinetic on Ubuntu, they only need to install three packages via
sudo apt install:
Once they are installed, follow instructions on RTAB-Map handheld RGB-D mapping tutorial to execute two ROS launch files. First one launches the ROS node to match the sensor device (in my case the Xbox 360 Kinect), second one launch RTAB-Map itself along with a visualization GUI.
I had fun scanning the shop environment where we hold our meetups. I moved the sensor around, both panning left-right and up-down, to get data from one side of the room. RTAB-Map created a pretty decent 3D representation of the shop. Here’s a camera view of one experiment. The Kinect is sitting on the workbench behind the laptop screen. The visualization GUI has the raw video image (upper left), an image with dots highlighting the features RTAB-Map is tracking (lower left), and a big window with 3D point cloud compilation of Kinect data.
Here’s the screenshot. It is even more impressive in person because we could interact with the point cloud window, rotate and zoom in 3D space to see the area from angles that the Kinect was never at. Speaking of which, look at the light teal line drawn in the lower right: this represents what RTAB-Map reconstructed as the path (in three dimensional space) I waved the Kinect through.
RTAB-Map is a lot of fun to play with, and shows huge potential for robot project applications.
Depth sensor data from a Xbox 360 Kinect might be imperfect, but it is good enough to proceed with learning about how a machine can make sense of its environment using such data. This is an area of active research with lots of options on how I can sample the current state of the art. What looks the most interesting to me right now is RTAB-Map, from Mathieu Labbé of IntRoLab at Université de Sherbrooke (Quebec, Canada.)
RTAB stands for Real-Time Appearance-Based, which neatly captures the constraint (fast enough to be real-time) and technique (extract interesting attributes from appearance) of how the algorithm approaches mapping an environment. I learned of this algorithm by reading the research paper behind a Hackaday post of mine. The robot featured in that article used RTAB-Map to generate its internal representation of its world.
The more I read about RTAB-Map, the more I liked what I found. Its home page lists events dating back to 2014, and the Github code repository show updates as recently as two days ago. It is encouraging to see continued work on this project, instead of something that was abandoned years ago when its author graduated. (Unfortunately quite common in the ROS ecosystem.)
Speaking of ROS, RTAB-Map itself is not tied to ROS, but the lab provides a rtab-ros module to interface with ROS. Its Github code repository has also seen recent updates, though it looks like a version for ROS Melodic has yet to be generated. This might be a problem later… but right now I’m still exploring ROS using Kinetic so I’m OK in the short term.
As for sensor support, RTAB-Map supports everything I know about, and many more that I don’t. I was not surprised to find support for OpenNI and OpenKinect (
freenect). But I was quite pleased to see that it also supports the second generation Xbox One Kinect via
freenect2. This covers all the sensors I care about in the foreseeable future.
The only downside is that RTAB-Map requires significantly more computing resources than what I’ve been playing with to date. This was fully expected and not a criticism of RTAB-Map. I knew 3D data would far more complex to process, but I didn’t know how much more. RTAB-Map will be my first solid data point. Right now my rover Sawppy is running on a Raspberry Pi 3. According to RTAB-Map author, a RPi 3 could only perform updates four to five times a second. Earlier I had outlined the range of computing power I might summon for a rover brain upgrade. If I want to run RTAB-Map at a decent rate, it looks like I have to go past Chromebook level of hardware to Intel NUC level of power. Thankfully I don’t have to go to the expensive realm of NVIDIA hardware (either a Jetson board or a GPU) just yet.