C++ source code and datasets from my PhD research

License - Download - Browse Source - Pre-requisites - Features - Install - Binaries - Datasets - Parameter documentation - Coming Soon - Known issues - Associated publications

License

License: my code is free for any use. the two closed-form 5-point essential matrix solvers are for academic use only (based on MATLAB and C code from Henrik Stewenius), also includes code based on Edward Rosten's FAST code with BSD license. Please email ( ) and let me know if you find it useful or find any bugs or can't get something working.

Download

Works in Linux, using gcc and the Netbeans ID for c++. May work in Windows (Windows/VS solution files are not currently maintained).

BoWSLAM currently only works in Linux. Important Additional instructions here.

Linux: Code now on Github:

git clone https://github.com/kayak-tom/tom-cv/

Windows: Use Github Windows

Try the latest revision first, revision 1069 may work on older systems (with boost 1.39.0 or earlier).

Browse Source

Pre-requisites:

gcc or clang, boost (version 1.54 or later, compiled), Eigen 3.1 or later (matrix library). OpenCV (version 2.4 or later)

Features

(more details here)

BaySAC (and RANSAC and PROSAC and SimSAC and WaldSAC) for Essential Matrix estimation and Homography estimation. Topdown refinement of solution. Nonlinear refinement of E on hypothesis sets found by RANSAC. Example...

Fast 5-point Essential Matrix estimation and refinement.

Estimate Homography and decompose into translation, rotation, plane-normal (Levenberg-Marquardt)

Bag-of-Words library: Real-time performance and dynamic retraining. Supports 10k+ images. Fast pairwise correspondences.

K-medoids clustering.

Real-time mosaicing (generates a locally accurate seamless mosaic in real-time. Does not generate a globally accurate mosaic you need to do incremental bundle adjustment to do this!)

Install

See License_and_Installation_Readme.txt. To use one simple function just copy the relevent code. To build libraries use cmake

Install in Linux

Make sure include files and libraries for boost, Eigen, and OpenCV (if needed) are visible to gcc. If Eigen is not visible then make a symlink to it in your checkout dir: ln -s PATH_TO_YOUR_EIGEN_DIR Eigen

Install in Linux (use cmake)

cd to the tom-cv directory.

mkdir build

cd build

cmake ..

make -j8

Install in Windows

Use cmake GUI (not fully tested)

You can use the Bag-of-Words code from any programming language under Windows

Datasets

Four datasets are available via my google docs account (too large to put here). To download them please email me ( ) and I will provide access. Most are low framerate, fairly high resolution and from a single camera, and some have GPS ground truth (as NMEA data or in an openoffice spreadsheet) and camera calibration data.

  • Aerial images for mosaicing: A sequence of 1610 aerial colour images sized 800x533 captured from a Canon SLR camera on a microlight over Motueka, NZ. Includes farmland, roads, shoreline, forest and urban areas. No GPS. Sample...
  • Suburban dataset for Single Camera SLAM: 3864 greyscale images captured at 640x426 from a Canon 400D SLR camera, at 2.8Hz. 2.5km looping trajectory around suburban Christchurch, including pedestrians and vehicles. Sample...
  • Helmetcam dataset for Single Camera SLAM: 2996 greyscale images sized 640x320 from a mountin bike helmet cam. Features poor light, motion blur, low-grade (low cost) optics, rapid, erratic 6DOF motion. Sample...
  • Aerial images for mosaicing: A sequence of 1115 640x480 colour images captured at 2Hz from a Canon SLR camera on a UAV over Antarctic ice sheets. No GPS, few strong features. Sample...

Parameter documentation

Documentation for all parameters is online here. Some headers are documented, e.g. ransac.h for BaySAC, E estimation, H estimation.

Coming soon

1) Seperate out clustering functionality (CLARA k-medoids)

2) More examples and documentation :-)

Known issues

BoWSLAM has additional requirements (and does not work reliably with gcc versions 4.4 or earlier, or early versions of boost)

K-medoids has hard-coded limit of 450 data-points in Windows, and stack allocation will fail with more than about 1000 in Linux.

 

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