Improving LiDAR Odometry with Hausdorff Distance-Based Variance Estimation

This paper<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> introduces a novel variance estimation method for LiDAR Odometry and Mapping (LOAM) using the Hausdorff Distance (HD) metric to enhance sensor fusion accuracy. Traditional LOAM and Iterative Closest Point (ICP) algorithms rely on optimization without providing variance estimation, limiting their reliability or using optimization error as variance, which can be optimistic in case of wrong convergences in sensor fusion tasks. Instead, by leveraging the HD as another performance metric to assess transformation accuracy and integrating the estimated variance into a Minimum Variance Unbiased Estimator (MVUE), the proposed method dynamically adjusts the confidence assigned to LOAM updates without need for any parameter tuning. This approach improves pose estimation when fusing LiDAR data with IMU and GNSS sensors. Experimental results on the MULRAN dataset demonstrate that HD-based variance estimation significantly reduces localization errors compared to direct odometry outputs. These findings establish Hausdorff Distance as a reliable variance metric, contributing to more robust and adaptive localization in autonomous systems.