{"title":"基于 6-DoF 激光雷达的自主飞行器快速稳健定位,抵御传感器误差","authors":"Gyu-Min Oh;Seung-Woo Seo","doi":"10.1109/LRA.2024.3457370","DOIUrl":null,"url":null,"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; \n<inline-formula><tex-math>$x$</tex-math></inline-formula>\n, \n<inline-formula><tex-math>$y$</tex-math></inline-formula>\n, and \n<inline-formula><tex-math>$heading$</tex-math></inline-formula>\n) and rely on prior maps and IMU for estimating the \n<inline-formula><tex-math>$roll$</tex-math></inline-formula>\n, \n<inline-formula><tex-math>$pitch$</tex-math></inline-formula>\n, and \n<inline-formula><tex-math>$z$</tex-math></inline-formula>\n 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 \n<inline-formula><tex-math>$roll$</tex-math></inline-formula>\n, \n<inline-formula><tex-math>$pitch$</tex-math></inline-formula>\n, and \n<inline-formula><tex-math>$z$</tex-math></inline-formula>\n 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 \n<inline-formula><tex-math>$x$</tex-math></inline-formula>\n, \n<inline-formula><tex-math>$y$</tex-math></inline-formula>\n, and \n<inline-formula><tex-math>$heading$</tex-math></inline-formula>\n coordinates. Subsequently, it optimizes the estimation of \n<inline-formula><tex-math>$roll$</tex-math></inline-formula>\n, \n<inline-formula><tex-math>$pitch$</tex-math></inline-formula>\n, and \n<inline-formula><tex-math>$z$</tex-math></inline-formula>\n 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.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy\",\"authors\":\"Gyu-Min Oh;Seung-Woo Seo\",\"doi\":\"10.1109/LRA.2024.3457370\",\"DOIUrl\":null,\"url\":null,\"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; \\n<inline-formula><tex-math>$x$</tex-math></inline-formula>\\n, \\n<inline-formula><tex-math>$y$</tex-math></inline-formula>\\n, and \\n<inline-formula><tex-math>$heading$</tex-math></inline-formula>\\n) and rely on prior maps and IMU for estimating the \\n<inline-formula><tex-math>$roll$</tex-math></inline-formula>\\n, \\n<inline-formula><tex-math>$pitch$</tex-math></inline-formula>\\n, and \\n<inline-formula><tex-math>$z$</tex-math></inline-formula>\\n 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 \\n<inline-formula><tex-math>$roll$</tex-math></inline-formula>\\n, \\n<inline-formula><tex-math>$pitch$</tex-math></inline-formula>\\n, and \\n<inline-formula><tex-math>$z$</tex-math></inline-formula>\\n 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 \\n<inline-formula><tex-math>$x$</tex-math></inline-formula>\\n, \\n<inline-formula><tex-math>$y$</tex-math></inline-formula>\\n, and \\n<inline-formula><tex-math>$heading$</tex-math></inline-formula>\\n coordinates. Subsequently, it optimizes the estimation of \\n<inline-formula><tex-math>$roll$</tex-math></inline-formula>\\n, \\n<inline-formula><tex-math>$pitch$</tex-math></inline-formula>\\n, and \\n<inline-formula><tex-math>$z$</tex-math></inline-formula>\\n 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. 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Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy
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.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.