Asynchronous Multiple LiDAR-Inertial Odometry using Point-wise Inter-LiDAR Uncertainty Propagation

TL;DR

This paper introduces an asynchronous LiDAR-inertial odometry method that models inter-sensor uncertainty using continuous-time IMU data, enabling integration of multiple LiDARs without strict synchronization.

Contribution

It proposes a novel point-wise uncertainty propagation approach using continuous-time IMU modeling to improve multi-LiDAR odometry accuracy and flexibility.

Findings

Validated on public and proprietary datasets.

Compatible with various LiDAR types and scanning patterns.

Reduces the need for strict time synchronization.

Abstract

In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common practice is to transform points among sensors while requiring strict time synchronization or approximating transformation among sensor frames. Unlike existing methods, we elaborate the inter-sensor transformation using continuous-time (CT) inertial measurement unit (IMU) modeling and derive associated ambiguity as a point-wise uncertainty. This uncertainty, modeled by combining the state covariance with the acquisition time and point range, allows us to alleviate the strict time synchronization and to overcome FOV difference. The proposed method has been validated on both public and our datasets and is compatible with various LiDAR manufacturers and scanning patterns. We open-source the code for public access at https://github.com/minwoo0611/MA-LIO.