Indoor Localization of Quadcopters in Industrial Environment

Abstract

The research work in this paper was carried out to reach advanced positioning capabilities of unmanned aerial vehicles (UAVs) for indoor applications. The paper includes the design of a quadcopter and the implementation of a control system with the capability to position the quadcopter indoor using onboard visual pose estimation system, without the help of GPS. The project also covered the design and implementation of quadcopter hardware and the control software. The developed hardware enables the quadcopter to raise at least 0.5kg additional payload. The system was developed on a Raspberry single-board computer in combination with a PixHawk flight controller. OpenCV library was used to implement the necessary computer vision. The Open-source software-based solution was developed in the Robotic Operating System (ROS) environment, which performs sensor reading and communication with the flight controller while recording data about its operation and transmits those to the user interface. For the vision-based position estimation, pre-positioned printed markers were used. The markers were generated by ArUco coding, which exactly defines the current position and orientation of the quadcopter, with the help of computer vision. The resulting data was processed in the ROS environment. LiDAR with Hector SLAM algorithm was used to map the objects around the quadcopter. The project also deals with the necessary camera calibration. The fusion of signals from the camera and from the IMU (Inertial Measurement Unit) was achieved by using Extended Kalman Filter (EKF). The evaluation of the completed positioning system was performed with an OptiTrack optical-based external multi-camera measurement system. The introduced evaluation method has enough precision to be used to investigate the enhancement of positioning performance of quadcopters, as well as fine-tuning the parameters of the used controller and filtering approach. The payload capacity allows autonomous material handling indoors. Based on the experiments, the system has an accurate positioning system to be suitable for industrial application.

A Review on the State of the Art in Copter Drones and Flight Control Systems

“…Environmental Monitoring. Drones with computer vision capabilities contribute to environmental monitoring by detecting and analyzing changes in ecosystems, wildlife habitats, and natural resources [ 213 ]. This is crucial for applications like biodiversity assessment, deforestation monitoring, and climate research.…”

A Study on Centralised and Decentralised Swarm Robotics Architecture for Part Delivery System

“…External markers can be utilised to provide robustness and accuracy of the localisation process. In the work of [12], ArUco markers are utilised, for the precise localisation of the camera, based on the average position from different landmarks. However, high-performance onboard image processing capabilities are required, to apply the appropriate machine vision algorithm in real-time to extract the position of the corners, while also providing correction.…”