Localizability estimation using correlation on occupancy grid maps

IF 1.5 Q3 INSTRUMENTS & INSTRUMENTATION

ROBOMECH Journal Pub Date : 2023-09-14 DOI:10.1186/s40648-023-00256-w

Maiku Kondo, Masahiko Hoshi, Yoshitaka Hara, Sousuke Nakamura

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引用次数: 0

Abstract

Abstract In the field of autonomous mobile robotics, reliable localization is important. However, there are real environments where localization fails. In this paper, we propose a method to estimate localizability based on occupancy grid maps. The localizability indicates the reliability of localization. There are several approaches to estimate localizability, we propose a method to estimate localizability as a covariance matrix of the Gaussian distribution using local map correlation. Our method can estimate the magnitude of the localization error and the characteristics of the error. To confirm the effectiveness of the proposed method, we constructed simulation environments that include representative shapes of indoor environments. We conducted an experiment to investigate the characteristics of the distribution of local map correlation. Furthermore, we also conducted an experiment of our method to estimate localizability on occupancy grid maps. The simulation experiment results showed that the proposed method could estimate the magnitude of the localization error and the characteristics of the error on occupancy grid maps. The proposed method was confirmed to be effective in estimating localizability.

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基于相关性的占用网格地图定位估计

在自主移动机器人领域，可靠的定位非常重要。然而，在现实环境中，本地化是失败的。本文提出了一种基于占用网格地图的可定位性估计方法。localizability表示定位的可靠性。估计可定位性的方法有很多种，我们提出了一种利用局部图相关性作为高斯分布的协方差矩阵来估计可定位性的方法。该方法可以估计出定位误差的大小和误差的特性。为了验证所提出方法的有效性，我们构建了包含室内环境代表性形状的仿真环境。我们通过实验研究了局部地图相关分布的特点。此外，我们还对该方法在占用网格地图上的可定位性进行了估计实验。仿真实验结果表明，该方法可以估计出占用网格地图的定位误差大小和误差特性。实验结果表明，该方法对定位能力的估计是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ROBOMECH Journal Mathematics-Control and Optimization

CiteScore

3.20

自引率

7.10%

发文量

审稿时长

13 weeks

期刊介绍： ROBOMECH Journal focuses on advanced technologies and practical applications in the field of Robotics and Mechatronics. This field is driven by the steadily growing research, development and consumer demand for robots and systems. Advanced robots have been working in medical and hazardous environments, such as space and the deep sea as well as in the manufacturing environment. The scope of the journal includes but is not limited to: 1. Modeling and design 2. System integration 3. Actuators and sensors 4. Intelligent control 5. Artificial intelligence 6. Machine learning 7. Robotics 8. Manufacturing 9. Motion control 10. Vibration and noise control 11. Micro/nano devices and optoelectronics systems 12. Automotive systems 13. Applications for extreme and/or hazardous environments 14. Other applications