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类似于大脑的位置测量方法基于改进的光流算法

Xiaochen Liu¹, Jun Tang², Chong Shen²

¹Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, School of Instrument and Electronics, North University of China, Taiyuan 030051, PR China; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science & Engineering, Southeast University, Nanjing 210096, PR China.

ISA transactions

|September 20, 2023

概括

这项研究引入了一个类似于大脑的导航系统,使用模糊内核C-means (FKCM) 集群和光流来准确测量车辆位置. 该方法通过模仿动物大脑细胞并纠正累积的错误来提高准确性.

科学领域:

机器人技术和自主系统
计算机视觉计算机视觉
计算神经科学是一种神经科学.

背景情况:

车辆定位传统上依赖于GPS或复杂的传感器融合.
现有的视觉测距方法可能会遭受累积错误和单数值的影响.
生物启发的导航机制提供了增强可靠性和准确性的潜力.

研究的目的:

开发一种纯粹的视觉,类似于大脑的导航方案,用于准确测量车辆位置.
将模糊内核C-means (FKCM) 集群与金字塔Lucas Kanade (LK) 光流算法集成.
为智能导航利用速度和位置单元的概念.

主要方法:

一个新的类似大脑的导航机制,灵感来自动物的速度和位置细胞.
使用金字塔卢卡斯·卡纳德 (LK) 的光流算法进行运动估计.
使用模糊内核C-means (FKCM) 聚类来消除光流计算中的奇点值.
整合速度测量和应用类似大脑的方案来纠正位置错误.

主要成果:

FKCM算法有效地消除了单数值,提高了速度的准确性.
拟议的类似大脑的导航方案大大减少了累积的位置测量误差.
实验结果表明,在位置测量方面,与经典的金字塔LK算法相比,其性能优越.

结论:

开发的纯视觉脑式导航方法提高了视觉导航的准确性和智能性.
FKCM辅助的金字塔LK算法提供了更强大,更准确的速度估计.
这种方法为先进的自动驾驶汽车导航系统提供了一个有希望的方向.

关键词:

类似大脑的导航系统卢卡斯卡内德的算法光学流的光学流量位置测量测量的位置测量

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