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一个改进的自适应式蒙特卡洛定位算法与虚拟运动模型集成

Cili Zuo¹, Demin Xie¹, Lianghong Wu^1,2

¹School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.

Sensors (Basel, Switzerland)

|April 26, 2025

概括

本研究介绍了一种改进的自适应式蒙特卡洛定位 (AMCL) 算法,使用正常分布变换 (NDT) 和扩展卡尔曼波器 (EKF). 改进的AMCL算法提高了移动机器人的定位速度和准确性,特别是在冷启动过程中.

科学领域:

机器人技术机器人技术机器人技术
移动机器人定位定位
传感器融合式传感器

背景情况:

适应式蒙特卡洛定位 (AMCL) 算法通常对距离测量有很高的依赖.
编码器错误和轮子滑动可能会对传统AMCL中的运动状态估计产生负面影响.
准确和稳定的移动机器人定位对于自主导航至关重要.

研究的目的:

提出一个改进的AMCL算法,以减少公里测量依赖.
为了提高定位的准确性,稳定性和速度,特别是在冷启动阶段.
解决现有AMCL方法在现实世界机器人应用中的局限性.

主要方法:

整合正常分布转换 (NDT) 用于点云匹配和虚拟位移估计.
在AMCL框架内引入虚拟运动模型,用于在没有机器人运动的情况下进行姿势更新.
扩展卡尔曼波器 (EKF) 的应用,以接轮计程表和惯性测量单元 (IMU) 数据,以进行可靠的位移估计.

主要成果:

拟议的算法在冷启动阶段证明了更好的本地化速度.
在整个本地化过程中观察到增强的本地化准确性和稳定性.
在模拟和真实环境中的实验验证证证了算法的有效性与现有方法相比.

结论:

基于NDT和EKF的改进AMCL算法有效地减轻了距离测量依赖.
该方法为移动机器人本地化性能提供了显著的进步.
这种方法为提高自主移动机器人的可靠性提供了一个有希望的解决方案.

关键词:

适应式的蒙特卡洛本地化扩展的卡尔曼过器移动机器人的本地化正常分布的变换是正常分布的变换.

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