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).
The code of Point-LIO is released! Readme will be updated step by step.
The related paper is accepted to Advanced Intelligent Systems(AIS), and is in the process of production. The preprint version is attached here, Point-LIO_preprint.pdf
Our accompany videos are available on youtube
If you have any questions about this work, please feel free to contact me <hdj65822ATconnect.hku.hk> and Dr. Fu Zhang <fuzhangAThku.hk> via email.