Open Access

ARTICLE

Unknown Environment Measurement Mapping by Unmanned Aerial Vehicle Using Kalman Filter-Based Low-Cost Estimated Parallel 8-Beam LIDAR

by Mohamed Rabik Mohamed Ismail¹, Muthuramalingam Thangaraj^1,*, Khaja Moiduddin^2,*, Zeyad Almutairi^2,3, Mustufa Haider Abidi²

1 Department of Mechatronics Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu District, Tamil Nadu, 603203, India
2 Advanced Manufacturing Institute, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
3 Department of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia

* Corresponding Authors: Muthuramalingam Thangaraj. Email: ; Khaja Moiduddin. Email:

Computers, Materials & Continua 2024, 80(3), 4263-4279. https://doi.org/10.32604/cmc.2024.055271

Received 22 June 2024; Accepted 30 July 2024; Issue published 12 September 2024

Abstract

The measurement and mapping of objects in the outer environment have traditionally been conducted using ground-based monitoring systems, as well as satellites. More recently, unmanned aerial vehicles have also been employed for this purpose. The accurate detection and mapping of a target such as buildings, trees, and terrains are of utmost importance in various applications of unmanned aerial vehicles (UAVs), including search and rescue operations, object transportation, object detection, inspection tasks, and mapping activities. However, the rapid measurement and mapping of the object are not currently achievable due to factors such as the object’s size, the intricate nature of the sites, and the complexity of mapping algorithms. The present system introduces a cost-effective solution for measurement and mapping by utilizing a small unmanned aerial vehicle (UAV) equipped with an 8-beam Light Detection and Ranging (LiDAR) system. This approach offers advantages over traditional methods that rely on expensive cameras and complex algorithm-based approaches. The reflective properties of laser beams have also been investigated. The system provides prompt results in comparison to traditional camera-based surveillance, with minimal latency and the need for complex algorithms. The Kalman estimation method demonstrates improved performance in the presence of noise. The measurement and mapping of external objects have been successfully conducted at varying distances, utilizing different resolutions.

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Keywords

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