Vision-based heading estimation for navigation of a micro-aerial vehicle in GNSS-denied staircase environment using vanishing point

Q3 Earth and Planetary Sciences Aerospace Systems Pub Date : 2024-03-30 DOI:10.1007/s42401-024-00282-5
B. Anbarasu
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Abstract

Micro-aerial vehicles (MAVs) find it extremely difficult to navigate in GNSS-denied indoor staircase environments with obstructed Global navigation satellite system (GNSS) signals. To avoid hitting both static and moving obstacles, MAV must estimate its position and heading in the staircase indoor scenes. In order to detect vanishing points and estimate heading for MAV navigation in a staircase environment, five different input colour space image frames—namely RGB image into a grayscale image and RGB image into hyper-opponent colour space—O1, O2, O3, and Sobel R channel image frames—have been used in this work. To determine the position and direction of the MAV, the Hough transform technique and K-means clustering algorithm have been incorporated for line and vanishing point recognition in the staircase image frames. The position of the vanishing point detected in the staircase image frames indicates the position of the MAV (Centre, left or right) in the staircase. In addition, to compute the heading of MAV, the Euclidean distance between the staircase picture centre, mid-pixel coordinates at the image’s last row, and the detected vanishing point pixel coordinates in the succeeding staircase image frames are used. The position and heading measurement can be utilised to send the MAV a suitable control signal and align it at the centre of the staircase when it deviates from the centre. The integrated Hough transform technique and K-means clustering-based vanishing point detection are suitable for real-time MAV heading measurement using the O2 channel staircase image frames for indoor MAVs with a high accuracy of ± 0.15° when compared to the state-of-the-art grid-based vanishing point detection method heading accuracy of ± 1.5°.

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基于视觉的航向估计,利用消失点在全球导航卫星系统失效的阶梯环境中为微型飞行器导航
在全球导航卫星系统(GNSS)信号受阻的室内楼梯环境中,微型飞行器(MAV)的导航极为困难。为了避免撞上静态和移动的障碍物,微型飞行器必须估计自己在楼梯室内场景中的位置和航向。为了检测消失点并估计无人飞行器在阶梯环境中的导航航向,本研究采用了五种不同的输入色彩空间图像帧--即 RGB 图像转换成灰度图像,以及 RGB 图像转换成超对立色彩空间--O1、O2、O3 和 Sobel R 通道图像帧。为了确定飞行器的位置和方向,采用了 Hough 变换技术和 K-means 聚类算法来识别阶梯图像帧中的线条和消失点。在阶梯图像帧中检测到的消失点位置表示飞行器在阶梯中的位置(中心、左侧或右侧)。此外,为了计算 MAV 的航向,使用了阶梯图像中心、图像最后一行的中间像素坐标与后续阶梯图像帧中检测到的消失点像素坐标之间的欧氏距离。位置和航向测量结果可用于向飞行器发送适当的控制信号,并在飞行器偏离阶梯中心时将其对准阶梯中心。综合 Hough 变换技术和基于 K-means 聚类的消失点检测适用于使用 O2 信道阶梯图像帧对室内无人飞行器进行实时航向测量,与最先进的基于网格的消失点检测方法的± 1.5°航向精度相比,其精度高达± 0.15°。
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来源期刊
Aerospace Systems
Aerospace Systems Social Sciences-Social Sciences (miscellaneous)
CiteScore
1.80
自引率
0.00%
发文量
53
期刊介绍: Aerospace Systems provides an international, peer-reviewed forum which focuses on system-level research and development regarding aeronautics and astronautics. The journal emphasizes the unique role and increasing importance of informatics on aerospace. It fills a gap in current publishing coverage from outer space vehicles to atmospheric vehicles by highlighting interdisciplinary science, technology and engineering. Potential topics include, but are not limited to: Trans-space vehicle systems design and integration Air vehicle systems Space vehicle systems Near-space vehicle systems Aerospace robotics and unmanned system Communication, navigation and surveillance Aerodynamics and aircraft design Dynamics and control Aerospace propulsion Avionics system Opto-electronic system Air traffic management Earth observation Deep space exploration Bionic micro-aircraft/spacecraft Intelligent sensing and Information fusion
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