/LIMO-Velo

A real-time, direct and tightly-coupled LiDAR-Inertial SLAM for high velocities with spinning LiDARs

Primary LanguageC++

LIMO-Velo [Alpha]

🔴 [16 February 2022] 🔴 The project is on 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 andreu.huguet@estudiantat.upc.edu for questions or ideas.

A real-time, direct and tightly-coupled LiDAR-Inertial SLAM that works (surprisingly) well under high velocities - even with spinning LiDARs.

Perfomance of the algorithm
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.

Perfomance of the algorithm
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.

Map comparison - Cones
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.

Map comparison - Cones
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

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.
  • heuristic 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.

Sample datasets

Xaloc's fast dataset
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 and a fast run in this Dropbox.

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.

References

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

TODO list

Urgent fixes

  • Rethink mapping_offline (see Discussions)
  • Investigate why Livox underdelivers compared to Fast-LIO2.

Design choices

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