Final

The report .pdf ,the video .wmv and repo.

I have been debugging a weird bug for a week. It causes random segmentation fault for no obvious reason. Since I've decided to reinvent the wheel somehow, to start from the very beginning and use opencv and pcl to build a casual version of the project, life is hard for every tiny problem including the incompatibility of pcl-1.1 with pcl-1.5, opencv2.3 and opencv2.0. The result so far is far from satisfactory.

OpenCV

There's some problems in integrating CUDA SURF with ROS, so I am not shifting to OpenCV GPU support. The libopencv2.3 was not compiled with CUDA support thus need to recompile OpenCV 2.3 with CUDA flag.

Now mainly testing offline as ROS provides rosbag to log and play data for realtime processing and debugging. The CUDA SURF needs roughly 0.02s for SURF detection and matching, while needs 0.08s for image loading and writing on 640x480 RGB8 images.

Continuous SURF detection and matching 

GPU-Accelerated Kinect Processing Presentation Downloads

Downloads

Presentation slides: pdf (1MB)    pptx(44MB)

Presentation voice-over: wmv

Midpoint Check

To improve the performance of real-time implementation of RGB-D 3D environment reconstruction, the following algorithm could be considered to implement with CUDA.
1. SURF(Speeded Up Robust Feature) detector/descriptor/matching
2. RANSAC
3. ICP(Iterative Cloud Point)

By midpoint I have basically done testing GPU based SURF algorithm and modify the interface to be integrated in ROS. It relies on OpenCV, OpenSurf, CUDPP and CUDA SURF libraries.

Next is to combine RANSAC pose estimation and ICP into sequential kernels with minimum memory transfer. Real-time rendering with OpenGL in rviz node should be considered as well.

The midpoint check presentation could be downloaded here.