Point-LIO

This is a fork of the original hku-mars/Point-LIO implementation and as such follows its licensing. If you use this implementation in your work, please cite the original authors. Our ctu-mrs implementation includes the following changes:

• Integration with the MRS UAV System.

About

The framework and key points of the Point-LIO.

Point-LIO is a robust and high-bandwidth LiDAR-inertial odometry with the capability to estimate extremely aggressive robotic motions. Point-LIO has two key novelties that enable a high-bandwidth LiDAR-inertial odometry (LIO). The first one is a point-by-point LIO framework, where the state is updated at each LiDAR point measurement without accumulating them into a frame. This point-by-point update allows an extremely high-frequency odometry output, significantly increases the odometry bandwidth, and also fundamentally removes the artificial in-frame motion distortion in aggressive motions. The second main novelty is a stochastic process-augmented kinematic model which models the IMU measurements as an output, instead of input as in existing filter-based odometry or SLAM systems, of the model. This new modeling method enables accurate localization and reliable mapping for aggressive motions even when IMU measurements are saturated. In experiments, Point-LIO is able to provide accurate, high-frequency odometry (4-8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s^{-1}) beyond the IMU measuring ranges. And Point-LIO is computationally efficient, robust, versatile on public datasets with general motions. As an odometry, Point-LIO could be used in various autonomous tasks, such as trajectory planning, control, and perception, especially in cases involving very fast ego-motions (e.g., in the presence of severe vibration and high angular or linear velocity) or requiring high-rate odometry output and mapping (e.g., for high-rate feedback control and perception).

Installation and launching

We recommend to install and launch Point-LIO from the mrs-ctu/mrs_point_lio_core repository. Follow the Readme.

Compatibility

The following descriptions have been removed from this Readme. Please see the original repository on how to use the features. *

• How to use Livox Avia, LiDARs with external IMU, Ouster/Velodyne.
• Saving maps to PCD files.
• Rosbag examples.

FAQ

• Make sure the IMU and LiDAR are Synchronized, that's important.
• Obtain the saturation values of your used IMU (i.e., accelerator and gyroscope), and the units of the accelerator of your used IMU, then modify the .yaml file according to those settings, including values of satu_acc, satu_gyro, and acc_norm. That's improtant.
• The warning message "Failed to find match for field 'time'." means that the timestamps of each LiDAR point are missing in the data. That is important since Point-LIO processes at the sampling time of each LiDAR point.
• We recommend to set the extrinsic_est_en = false if the extrinsic is given. As for the extrinsic initialization, please refer to: Robust and Online LiDAR-inertial Initialization.
• If you want to use Point-LIO without IMU, set the imu_en = false and provide a predefined value of gravity in gravity_init as true as possible in the .yaml file and keep the use_imu_as_input = 0.

TODOs:

• Nodetelize