Object Tracking with Sensor Fusion – An Interactive Learning Tool

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2024-01-01 DOI:10.1016/j.ifacol.2024.10.285

Andrei Moraru , Eva-H. Dulf

引用次数: 0

Abstract

Body tracking plays a key role in autonomous navigation applications. Behavior that resists inertia can be modelled as a dynamical system, wherein the kinematic component is constituted by the action of motion. Such a system may then be subjected to estimation algorithms and control laws formulated by systems theory, according to the specific problem domain for which it is modelled. This paper presents a detailed comparison of three main statistical algorithms for estimating dynamical system parameters: the linear, extended, and unscented Kalman filters. The body motion is intercepted by sensor fusion. To facilitate visual validation and concretization of the theoretical notions presented, a two-dimensional (2D) game-like graphical application has been developed to enhance user comprehension.

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利用传感器融合进行物体跟踪 - 一种互动学习工具

身体跟踪在自主导航应用中发挥着关键作用。抵抗惯性的行为可被建模为一个动力系统，其中运动成分由运动作用构成。这样的系统可以根据其建模的特定问题领域，采用系统理论制定的估计算法和控制法则。本文详细比较了用于估算动态系统参数的三种主要统计算法：线性、扩展和无香味卡尔曼滤波器。通过传感器融合拦截车身运动。为了便于直观验证和具体化所提出的理论概念，我们开发了一个类似于游戏的二维（2D）图形应用程序，以增强用户的理解能力。

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

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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