UA-LIO: An Uncertainty-Aware LiDAR-Inertial Odometry for Autonomous Driving in Urban Environments

Abstract

Odometry estimation is a fundamental technique for many robotics and autonomous driving applications. Recent advances using light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensor-fusion methods show promising potential in odometry estimation by combining the precise perception of LiDAR and the high-frequency motion estimation from IMU sensors. In this article, we propose a novel uncertainty-aware LiDAR-inertial odometry (LIO) algorithm designed for autonomous vehicles operating in urban driving environments. Unlike previous approaches that use point-to-point or point-to-plane methods for updates, in this article, we employ a distribution-to-distribution approach for updates. Each point sampled on a surface is modeled as a Gaussian distribution, and the covariance is estimated from the decomposed eigenvalues by considering the correlation between the current point and its surrounding points. The estimated covariance makes the update module aware of match quality, allowing it to ignore poorly matched points and focus on well-matched ones, thereby improving odometry accuracy. In addition, it eliminates Z-axis drift in long-term odometry estimation by using ground plane information and linearly adjusts pose uncertainty based on optimized pose values. This unified approach to managing uncertainty is essential for the system’s long-term stability and accuracy. We thoroughly evaluated our method using multiple publicly available datasets. The experimental results show that our method is accurate and reliable in dynamic urban environments and achieves state-of-the-art LIO performance with fast speed and strong generalization ability. We will release the code of our method here: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Gatsby23/UA-LIO.git</uri>.