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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Published on: May 7, 2019

在自主系统中先进的单眼户外姿势估计:利用光流,深度估计和语义分割与动态物体移除.

Alireza Ghasemieh¹, Rasha Kashef¹

¹Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)

|January 8, 2025

概括

此摘要是机器生成的。

本研究介绍了一种使用单眼相机的自适应视觉测距框架,用于自动驾驶汽车中的GPS独立定位. 它增强了复杂环境中的安全和导航,提供了一个具有成本效益的解决方案.

关键词:

深度估计估计的估计.光学流的光学流量构成估计估计的估计.语义细分语义细分语义细分语义细分视觉测距仪使用视觉测距仪.

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科学领域:

机器人技术和自主系统
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

全球定位系统 (GPS) 在受阻环境中的局限性要求自动驾驶汽车 (AV) 需要GPS独立的定位.
使用LiDAR或立体相机的现有解决方案通常是昂贵和复杂的.
单眼视觉提供了一种实用,具有成本效益的替代方案,但缺乏强大的姿势估计模型.

研究的目的:

开发一种新的适应性框架,用于户外姿势估计和安全导航,使用单眼相机的增强视觉测距 (VO).
为满足对强大,GPS独立的定位解决方案的需求,可适应各种平台和成本限制.
确保GPS被拒绝的地区的自动驾驶汽车的安全性和实时决策.

主要方法:

利用视觉距离测量 (VO) 来估计GPS拒绝环境中的图像序列的摄像头姿势.
开发了一个适应性框架,利用单眼视觉,先进的控制理论和机器学习.
集成的人工智能驱动模型以满足多传感器系统性能标准.

主要成果:

在KITTI测距数据集上,在姿势估计准确度方面取得了显著的改进.
证明了用于真实世界AV应用的成本效益高,稳健的解决方案.
通过综合控制理论,在复杂的交通场景中提高AV安全性和性能.

结论:

拟议的自适应视觉测距框架为自动驾驶汽车提供了可靠且具有成本效益的GPS独立定位解决方案.
这种方法提高了导航安全性和适应能力,在具有挑战性的环境中,传统的GPS无法使用.
该研究推进了自主系统的单眼视觉能力,为更广泛的采用铺平了道路.