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PHD Filter SLAM with CUDA
Cuda C++ Python Matlab Other
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cfg
matlab
python
src
.gitignore
LICENSE
README
build_and_debug.sh
build_and_run.sh
cuda-PHDSLAM.pro
debug.sh
run.sh

README

This project is still very much a work in progress; nevertheless, for the brave
souls who would like to attempt running this code here are some pointers:

--------Dependencies------
CUDA 3.2 or greater, including the CUDA-SDK
Boost 1.4.7 or greater
Qt 4.x.x (for compiling with qmake only)

-------Compiling---------
Run qmake to generate makefiles. Then, type 'make -w' to compile the code. The 
resulting executables will be placed in the bin/ directory. There are some
convenience shell scripts included for building and running in one go.

--------Configuring-------
Options are set in the file cfg/config.cfg. There is some documentation for the
different options at the top of the file. 

---------Data----------
At minimum, two text files for simulation data are required. One should contain
the range-bearing measurements, and the other should contain the Ackerman
steering control inputs. For both files, each line represents the inputs for a
particular time step.
 
Range-bearing measurements are grouped as follows:
Range1 Bearing1 Range2 Bearing2 ... RangeM BearingM

Optionally, two further text files may be specified which contain the
timestamps in seconds for the controls and measurements. They must have the same
number of lines as the respective data files.

---------Output-------------
Output log files are placed in a folder with the time of the simulation start.
The main files of interest are those names state_estimatexxxxx.log, which
consist of 5 lines:
1. The expected vehicle pose [x y heading vx vy vheading]
2. The estimated landmark map (MAP or EAP depending on config)
3. Log particle weights
4. Particle poses
5. Cardinality distribution (CPHD only)

The MATLAB script plotPhdSlam.m, found in the matlab/ folder, can assist in
visualizing the results.

Good Luck!
Chee Sing Lee
24 January 2012

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