Solutions for visually aided 2D/3D SLAM for UAV with ROS

edit

Hi Tom!

As Ivan already suggested, I would separate the problem of continuous motion estimation from the loop closing problem. For vehicle control you need velocity estimates at high rates. For short path following, you need pose estimates that do not "jump" as might occur in a loop closing event.

For the first part of the problem, a fast visual odometry combined with an IMU is a common approach. Apart from the already mentioned libviso2, fovis is a well-coded visual odometry library by MIT, University of Washington and Pontificia Universidad Catolica de Chile which gives more possibilities on introspection than libviso2 and supports RGBD and stereo cameras. I believe that this software is part of the full SLAM system you already mentioned in your question. I highly recommend reading the corresponding paper. We already wrapped libviso2 in ROS and started wrapping fovis, too. See the ROS packages viso2_ros and fovis_ros for details. fovis_ros only supports stereo so far but writing a node for kinect should be fairly simple. We get full frame rate (10Hz) motion estimates using a bumblebee stereo camera (2*1024x768px) with both libraries on an i3 processor. In small environments, the drift is acceptable and you can do something without a full SLAM algorithm.

If you get good motion estimates from your VO/IMU implementation, the second part of the problem -- the loop closing -- does not need to work at high rates. Every now and then, whenever you might be able to close a loop, you can start a pose graph optimization. You need much stronger features (SIFT, SURF etc.) than for visual odometry, as the matching constraints are far less. For the SLAM part, I suggest you to have a look at g2o (also available through the libg2o package: apt-get install ros-fuerte-libg2o), which is the backend of RGBDSLAM (and supposedly others).

There also exist combined approaches such as PTAM (parallel tracking and mapping, ROS wrapper here) that tightly integrate incremental motion estimation and mapping. Note that this algorithm uses monocular images only and therefore needs to be combined with other sensors to provide pose estimates at a metric scale.

Within the last year, a lot of solutions to this problem popped out. I would rather try a bunch, look at the code and adapt it to your needs than starting from scratch.

Good luck!

As a comment on the relative computational requirements of visual odometry with stereo versus Kinect (I don't have karma to comment yet), the Kinect odometry should be more lightweight since the depth information is 'free' (it comes from the Kinect) whereas in stereo you'll have to extract the depth information from the stereo images yourself. The libviso2 link that was linked is referring to monocular visual odometry, which would not be necessary under the Kinect.

Hello Tom,

I am waiting for ScaViSLAM to be realeased. I think it will provide a nice, lightweight and precise visual slam system for monocular cameras.

I spoke with Hauke (its creator) some months ago. He said he was planning to release it in spring. So I assume (and hope) it will be released quite soon now.

Raph

You can also check out ccny_rgbd, which provides tools for fast visual odometry with RGB-D cameras.

EDIT as per K_Yousif's question:

I'm not sure if this is also the case with RGBDSLAM, but in our implementation, we've separated the visual odometry from the loop-closing problem. We do provide a mapping interface, which operates on top of the visual odometry and can perform SLAM, but it is not required for the VO.

In terms of the VO, we use a Kalman-Filter based approach which does not require computation of feature descriptors or RANSAC matching. This allows us to use cheap features (such as Lucas-Kanade corners). On an i7 processor, I'm getting a processing time of ~10 to 15ms per frame. Also, our VO has constant time and space requirements (not sure if this is true with RGBDSLAM).

The wiki pages have a bit more information about the pipeline

There is now also the Multi-Resolution Surfel Map library available, which performs dense RGB-D-SLAM on a CPU.

code.google.com/p/mrsmap

Details are in this paper:

www.ais.uni-bonn.de/papers/JVCI_13_RGB-D-SLAM.pdf