Designed for easy modifying via modular and easy to understand code. Relying upon HKU-Mars's IKFoM and ikd-Tree open-source libraries.
- Initialize IMU measurements
- Remove unused
Publishers.hpp's methods. - Simplify the upsampling in the Compensator.
- Check if need to add point in map
- Renew Buffer private structure. Interesting answer in StackOverflow: https://stackoverflow.com/a/67236232
- Interpolation and smoothing of states when mapping offline
- Erase unused (potentially dangerous) points in the map
- Try to add a module for removing dynamic objects such as people or vehicles
- Use UKF instead of EKF for fun
- Add vision buffer and ability to paint the map's points
When cloning the repository, we also need to clone the IKFoM and ikd-Tree submodules. Hence we will use the --recurse-submodules tag.
git clone --recurse-submodules https://github.com/Huguet57/LIMO-Velo.git
We either can do catkin_make or catkin build to compile the code. By default it will compile it optimized already
To run LIMO-Velo, we can run the launch file roslaunch limovelo test.launch if we want a visualization or roslaunch limovelo run.launch if we want it without.
An additional launch file roslaunch limovelo debug.launch is added that uses Valgrind as a analysing tool to check for leaks and offers detailed anaylsis of program crashes.
To adapt LIMO-Velo to our own hardware infrastructure, a YAML file config/params.yaml is available and we need to change it to our own topic names and sensor specs.
Relevant parameters are real_time if you want to get real time experience, mapping_offline is on an alpha stage and it does not work 100% as it should of and the heuristic parameter which you can choose how you want the initialization of the pointcloud sizes (sizes =: deltas, in seconds).
TODO - This section is intended to explain how to modify the LiDAR driver to increase its frequency by publishing parts of the pointcloud instead of waiting for all of it.