Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

Castillo, Wilbert G. Aguilar; Casaliglla, Veronica P.; Polit, Jose L.

doi:10.3390/electronics6010010

Cited by 78 publications

(32 citation statements)

References 40 publications

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“…In Aguilar et al (2017), a monocular on-board camera is used. It compares the image obtained in real time from UAV with a database of known obstacles.…”

Section: Uav Sensing Methodsmentioning

confidence: 99%

UAV attitude estimation based on the dual filtering methods

Zhuang

Guo

Raj

et al. 2018

IJIUS

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Purpose A toy UAV performs tumbling, rolling, racing and other complex activities. It is based on low-cost hardware and hence requires a better algorithm to estimate the attitudes more accurately with low power consumption. The proposed technique based on optimized Madgwick filter and moving average filter (MAF) ensures improved convergence speed in estimating the attitude, achieves higher accuracy and provides robustness and stability of the toy UAV. The paper aims to discuss this issue. Design/methodology/approach Traditional methods are prone to problems such as slow convergence speed and errors in calculation of the attitude angles. These errors cause the vehicle to drift and tremble, thus affecting the overall stability of the vehicle. The proposed method combines the features of optimized Madgwick filter and MAF to provide better accuracy, achieved through the fusion of gyroscope and accelerometer data, and zero correction to eliminate the random drift error of the gyroscope and removal of high-frequency interference by MAF of the accelerometer data. The experimental results on actual flight data showed that the method was better than the conventional Madgwick and Mahony complementary filters. Findings The performance of the proposed method was analyzed by estimating the pitch and roll angles under the static and dynamic condition of the toy UAV. The results were compared with two traditional methods: Madgwick and Mahony complement filter. In the static condition, the variance and average error while estimating the attitudes was comparatively lower than the traditional method. For the dynamic conditions, the convergence time to achieve a prescribed swing angle was again lower than the traditional method. From these two experiments, it can be seen that the proposed method provides better attitude estimation at lower computation time. Originality/value The proposed method combines the optimized Madgwick filter and MAF to accuracy estimate the attitude of toy UAV. The algorithm mainly suits the toy UAVs which are based on low-cost hardware and require better control systems to ensure stability of the vehicle. The experimental results on real flight data illustrate that the method not only improves the convergence speed in estimating the attitude angle for large maneuvers of the toy UAV, but also achieves higher accuracy in the attitude estimation, thus ensuring the robustness and stability of the UAV.

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“…In Aguilar et al (2017), a monocular on-board camera is used. It compares the image obtained in real time from UAV with a database of known obstacles.…”

Section: Uav Sensing Methodsmentioning

confidence: 99%

UAV attitude estimation based on the dual filtering methods

Zhuang

Guo

Raj

et al. 2018

IJIUS

View full text Add to dashboard Cite

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“…As a future work, we extrapolate video freeze detection to onboard applications as people detection [28] [29], navigation [30] [31] [32], obstacle avoidance [33] [34] [35], and mapping [36].…”

Section: Discussionmentioning

confidence: 99%

Motion intention optimization for multirotor robust video stabilization

Aguilar

Ángulo

Pardo

2017

2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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Abstract-In this paper we present an optimization algorithm for simultaneously detecting video freeze and obtaining the minimum number of the frame required in motion intention estimation for real time robust video stabilization on multirotor unmanned aerial vehicles. A combination of a filter and a threshold is used to the video freeze detection, and for optimizing the algorithm, we find the minimum number of frames for motion intention estimation without decrease the performance.

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“…Unmanned aerial vehicles (UAVs) use vision as the principal [17] source of information through the monocular onboard camera. The system compares the obtained image to the obstacles to be avoided.…”

Section: Image Processing Obstacle Avoidance Navigationmentioning

confidence: 99%

Expanding Navigation Systems by Integrating It with Advanced Technologies

Domb¹

2020

Geographic Information Systems in Geospatial Intelligence

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Navigation systems provide the optimized route from one location to another. It is mainly assisted by external technologies such as Global Positioning System (GPS) and satellite-based radio navigation systems. GPS has many advantages such as high accuracy, available anywhere, reliable, and self-calibrated. However, GPS is limited to outdoor operations. The practice of combining different sources of data to improve the overall outcome is commonly used in various domains. GIS is already integrated with GPS to provide the visualization and realization aspects of a given location. Internet of things (IoT) is a growing domain, where embedded sensors are connected to the Internet and so IoT improves existing navigation systems and expands its capabilities. This chapter proposes a framework based on the integration of GPS, GIS, IoT, and mobile communications to provide a comprehensive and accurate navigation solution. In the next section, we outline the limitations of GPS, and then we describe the integration of GIS, smartphones, and GPS to enable its use in mobile applications. For the rest of this chapter, we introduce various navigation implementations using alternate technologies integrated with GPS or operated as standalone devices.

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Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

Cited by 78 publications

References 40 publications

UAV attitude estimation based on the dual filtering methods

UAV attitude estimation based on the dual filtering methods

Motion intention optimization for multirotor robust video stabilization

Expanding Navigation Systems by Integrating It with Advanced Technologies

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