- Clone the repo
- Compile the kd_tree_nearest_point.cpp
- Run the program
- Run suca.py to visualize the result
- The .csv files are used both for configuration and logging of the results
What you should do is to play around trying to change the Odometry values and the shape of the trajectory (by hand or in some other creative ways) and checking if the results are correct.
If you find some egde cases that brake the algoritm GREAT! Go ahead and fix them yourself.
After you have fixed all the bugs you should run some benchmarks to get some data on how fast the algorithm actually is.
Once you have done all that we can go ahead and proceed to the next step.
This code is just a starting point I wanted to give you, you are free to change everything you want or you can even don't use this code at all