Mobile robot

Pure mathematics

Feature (linguistics)

Dimension (graph theory)

Mathematics

Lidar

Inertial measurement unit

Algorithm

Mathematical analysis

Philosophy

Iterated function

Artificial intelligence

Kalman filter

Geography

Extended Kalman filter

Computer science

Linguistics

Computation

Odometry

Simultaneous localization and mapping

Computer vision

Remote sensing

Robot

IEEE Robotics and Automation Letters

This letter presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of a large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various indoor and outdoor environments. In all tests, our method produces reliable navigation results in real-time: running on a quadrotor onboard computer, it fuses more than 1200 effective feature points in a scan and completes all iterations of an iEKF step within 25 ms. Our codes are open-sourced on Github. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter