Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements

Abstract

In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello’s image frames.

Distributed safe formation tracking control of multiquadcopter systems using barrier Lyapunov function

“…Quadcopters are one of the most important categories of multirotor drones and consist of four arms, four motors, and four propellers. The control and navigation of quadcopters in a single or cooperative form have been the subject of various studies to enhance their capabilities for various applications ( Montazeri et al, 2021 ; Sadeghzadeh-Nokhodberiz et al, 2023 , 2021 ). A multiquadcopter system is a form of multiagent system that is used in various extreme environment applications, including nuclear decommissioning ( Martin et al, 2016 ; Allahyar and Koubaa, 2023 ), volcanology ( James et al, 2020 ), wildfire monitoring ( Julian and Kochenderfer, 2019 ), and underground mining ( Neumann et al, 2014 ).…”

“…A comprehensive review comparing various collision avoidance strategies for unmanned aerial vehicle (UAV) applications can be found in Yasin et al (2020) . In the context of consensus-based formation control, similar collision avoidance strategies were surveyed and discussed by Sadeghzadeh-Nokhodberiz et al (2023) . When two drones generate a formation, they may collide with each other and obstacles in the transient phase as well as when reaching their desired positions and orientations.…”

“…When two drones generate a formation, they may collide with each other and obstacles in the transient phase as well as when reaching their desired positions and orientations. The consensus-based collision-free methods reviewed in both Lizzio et al (2022) and Sadeghzadeh-Nokhodberiz et al (2023) can be grouped under four main categories: (i) optimization-based techniques, (ii) force-field or artificial potential field (APF) techniques, (iii) geometric approaches, and (iv) sense-and-avoid approaches. As reviewed in Sadeghzadeh-Nokhodberiz et al (2023) , each of these approaches has its advantages/disadvantages for application to real life.…”

“…The consensus-based collision-free methods reviewed in both Lizzio et al (2022) and Sadeghzadeh-Nokhodberiz et al (2023) can be grouped under four main categories: (i) optimization-based techniques, (ii) force-field or artificial potential field (APF) techniques, (iii) geometric approaches, and (iv) sense-and-avoid approaches. As reviewed in Sadeghzadeh-Nokhodberiz et al (2023) , each of these approaches has its advantages/disadvantages for application to real life. Operationally speaking, static situations wherein obstacles are known ahead of time or are picked up on by the entire formation are more suited for optimization-based techniques.…”