Slam algorithm implementation

For more substantial description please read the Szakdolgozat.pdf file.

Simulator

Perception simulator for driverless cars

Usage

python3 simulator.py [-h] [-d] [-n] [-p] input_file

Optional arguments

• -h, --help - shows help message and exit
• -d, --directions - shows directional vectors of the car
• -n, --noisy - uses noise for the output data
• -p, --perception - shows vectors to the cones the car is currently detecting

SLAM

SLAM algorithm implementation

Compiling

For normal use:

mkdir build
cd build
cmake ..
make -j

For checking the code coverage:

mkdir build
cd build
cmake -DCOVERAGE=1 ..
make -j

Usage

Executable file is created in the build/src folder.

./slam [-h] [-v] [-l] [-n] [-p] [-s] input_file output_file

Optional arguments

• -h, --help - shows help message and exits
• -v, --version - prints version information and exits
• -l, --loop_closure - enables loop closing for the optimization
• -n, --noise - uses noise on the input data
• -p, --plot - plots the results using matplotlib
• -s, --segmentation - enables the segmentation of the data

Testing

Executable files are created in their own folder. You can execute them separately or with the help of ctest from the build directory.

Tools

With the help of plot.py found in the folder tools you can plot the data generated with the slam executable. With the help of coverage.sh found in the folder tools you can generate a code coverage report for the dataenumerator and graph libraries and the misc headers.