LIMO-Velo

Contact me at andreu.huguet@estudiantat.upc.edu for questions or ideas.

News! 🎉

Now with a ROS2 branch, ros2 - thanks to bertaveira. Broader LiDARs support is needed.

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).

LiDARs supported

Velodyne
Hesai
Ouster
Livox (check livox git branch)

Dependencies

Ubuntu (tested on 18.04, 20.04)
ROS (tested on Melodic, Noetic)
Eigen
PCL (tested on 1.8)

Newest additions

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 `hdmaps`)

Prelocalization with a previoulsy saved HD map. Still work in progress on the hdmaps branch. Official release will be in a couple of days.

Sample datasets

Xaloc's "fast" dataset. High velocity in the straights (~15m/s) and tight turns (~80deg/s).

Try xaloc.launch with Xaloc's own rosbags.

🏁 Find a 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.

Using LIMO-Velo

0. Cloning the repository

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

1. Compiling the code

We either can do catkin_make or 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_time if you want to get real time experience.
mapping_offline is on an pre-alpha stage and it does not work 100% as it should of.
initialization which 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.

References

IKFoM: Iterated Kalman Filters on Manifolds
ikd-Tree: Incremental KD-Tree for Robotic Applications
FAST-LIO2: Fast and Direct LIO SLAM

TODO list

Fixes in progress

Work with 9-DOF IMUs
Saving and loading HD-Maps (needs 9-DOF done)
Adding GPS as extra input
Rethink mapping_offline (see Discussions)

Design choices

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

li9616/LIMO-Velo