Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy

Abstract

Precise and real-time localization is crucial for autonomous vehicles. State-of-the-art methods utilize 3D light detection and ranging (LiDAR), inertial measurement unit (IMU), and global positioning system (GPS). However, to meet real-time constraints, these methods often limit the search space to only three degrees of freedom (DoF; $x$ , $y$ , and $heading$ ) and rely on prior maps and IMU for estimating the $roll$ , $pitch$ , and $z$ coordinates. This reliance on maps and sensors can introduce inaccuracies if they contain errors. To achieve precise localization in scenarios where IMU or map errors are present, the $roll$ , $pitch$ , and $z$ coordinates must be estimated. However, incorporating these additional dimensions into the localization process may increase the processing time, rendering it unsuitable for real-time applications. Herein, we propose a precise and robust 6-DoF LiDAR localization algorithm. Instead of directly generating all 6-DoF, the proposed algorithm generates particles based on the $x$ , $y$ , and $heading$ coordinates. Subsequently, it optimizes the estimation of $roll$ , $pitch$ , and $z$ coordinates of each particle while maintaining a fixed number of particles. By expanding the dimensionality in this manner, we mitigate the accuracy degradation that may occur with 3-DoF positioning when dealing with faulty sensors or maps. Experimental results demonstrate that the proposed algorithm achieves satisfactory performance even in scenarios where sensor accuracy is compromised.