alpha stage, so be sure to open Issues and Discussions and give all the feedback you can! Better to have 20 people giving lots of feedback than 1000 not saying anything.
Contact me at
[email protected] for questions or ideas.
A real-time LiDAR-Inertial SLAM for high velocities in spinning LiDARs.
Visualization of the algorithm with
delta = 0.01 (100Hz)
Designed for easy modifying via modular and easy to understand code. Relying upon HKU-Mars's IKFoM and ikd-Tree open-source libraries. Based also on their FAST_LIO2.
Tested and made for racing at Formula Student Driverless
Common working speeds are 20m/s in straights and 100deg/s in the turns.
Tested on and made for Barcelona's own "Xaloc".
Centimeter-level accuracy is kept under racing speeds
Only algorithm that can deliver centimeter-level resolution on real-time. See the part of my thesis where I explain the algorithm and its results: LIMOVelo + Results.
Comparison of cones under racing speeds running all algorithms in real-time, except for LIO-SAM (-r 0.5). It failed otherwise.
Designed to be easily understood even by beginners
Developing an algorithm for a team requires the algorithm to be easy enough to understand being passed through generations.
LIMO-Velo's pipeline. Here are seen the different modules (blue), data (orange) and libraries (dark green).
- Livox (check
High Quality Maps
Sometimes a higher map quality is needed, now a new
high_quality_publish parameter has been added to yield results like this below.
Sometimes Xaloc needs more definition to see if a cluster of points is actually a cone.
Work in progress (branch
Prelocalization with a previoulsy saved HD map. Still work in progress on the
hdmaps branch. Official release will be in a couple of days.
Xaloc's "fast" dataset. High velocity in the straights (~15m/s) and tight turns (~80deg/s).
xaloc.launch with Xaloc's own rosbags.
slow(818MB) and a
fast(1.71GB) run in this Dropbox folder.
See Issue #10 to see other sample datasets found in the web. Don't hesitate to ask there for more data on specific scenarios/cases.
0. Cloning the repository
When cloning the repository, we also need to clone the IKFoM and ikd-Tree submodules. Hence we will use the
git clone --recurse-submodules https://github.com/Huguet57/LIMO-Velo.git
1. Compiling the code
We either can do
catkin build to compile the code. By default it will compile it optimized already
2. Running LIMO-Velo
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.
2.1 Debugging LIMO-Velo
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.
3. Changing parameters
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_timeif you want to get real time experience.
mapping_offlineis on an pre-alpha stage and it does not work 100% as it should of.
initializationwhich you can choose how you want the initialization of the pointcloud sizes (sizes =: deltas, in seconds).
4. Modifying the LiDAR driver to get true real-time performance
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.
- IKFoM: Iterated Kalman Filters on Manifolds
- ikd-Tree: Incremental KD-Tree for Robotic Applications
- FAST-LIO2: Fast and Direct LIO SLAM
Fixes in progress
- Work with 9-DOF IMUs
- Saving and loading HD-Maps (needs 9-DOF done)
- Adding GPS as extra input
- Renew Buffer private structure. Interesting answer in StackOverflow: https://stackoverflow.com/a/67236232
- Simplify the upsampling in the Compensator.
Fixes to investigate
- Interpolation and smoothing of states when mapping offline
- Erase unused (potentially dangerous) points in the map
- Check if need to add point in map
- Try to add a module for removing dynamic objects such as people or vehicles
- Use UKF instead of EKF
- Add vision buffer and ability to paint the map's points
- Initialize IMU measurements