A Height Estimation Approach for Terrain Following Flights from Monocular Vision

Campos, Igor S.G.; Nascimento, Erickson R.; Freitas, Gustavo; Chaimowicz, Luiz

doi:10.3390/s16122071

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…The automatic applications of UAVs rely on the continuous control of the position, attitude, speed and other high level commands [2,3]. Specifically, the attitude and height (the clearance from the ground) controllers allow terrain-following of an autonomous quadrotor, which is useful for topography prediction, navigation and obstacle avoidance [4][5][6]. An integrated navigation framework is an autonomous system used for determining the position, attitude and velocity of UAVs using multi-sensors [7,8], i.e., a global positioning system (GPS), an inertial measurement unit ((IMU) e.g., an accelerometer, a gyroscope and a magnetometer) [9] and radar or distance sensors [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

et al. 2021

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For the application of the autonomous guidance of a quadrotor from confined undulant ground, terrain-following is the major issue for flying at a low altitude. This study has modified the open-source autopilot based on the integration of a multi-sensor receiver (a Global Navigation Satellite System (GNSS)), a Lidar-lite (a laser-range-finder device), a barometer and a low-cost inertial navigation system (INS)). These automatically control the position, attitude and height (a constant clearance above the ground) to allow terrain-following and avoid obstacles based on multi-sensors that maintain a constant height above flat ground or with obstacles. The INS/Lidar-lite integration is applied for the attitude and the height stabilization, respectively. The height control is made by the combination of an extended Kalman filter (EKF) estimator and a cascade proportional-integral-derivative (PID) controller that is designed appropriately for the noise characteristics of low accuracy sensors. The proposed terrain-following is tested by both simulations and real-world experiments. The results indicate that the quadrotor can continuously navigate and avoid obstacles at a real-time response of reliable height control with the adjustment time of the cascade PID controller improving over 50% than that of the PID controller.

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Section: Introductionmentioning

confidence: 99%

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

et al. 2021

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“…Recent advances in unmanned aerial vehicle (UAV) technologies have produced low-cost and high-mobility UAVs, rapidly broadening their real-world civil engineering application [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. For example, aerial images taken by UAVs have been used to construct three-dimensional structural models [ 8 , 9 , 10 , 11 ], evaluate road conditions [ 12 , 13 , 14 ], and conduct traffic surveillance and management [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Kim

Lee

Ahn

et al. 2017

Sensors

176

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Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

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“…In order to solve the shortcomings of single sensor detection, some researchers applied multi-sensor information fusion methods to fuse data from senso he Extended Kalman Filter (EKF). Based on this, Campos et al [24] fused the data form a GPS, an IMU and an optical flow sensor to obtain estimated altitudes. The results illustrated that when the UAV flew at a constant speed and the flight altitude was 5m, the detection altitude error was 0.135 m. However, if the drone accelerates, the detection accuracy fluctuates greatly.…”

Section: Introductionmentioning

confidence: 99%

Detection of crop heights by UAVs based on the Adaptive Kalman Filter

Xu¹,

Wang²,

Yang³

et al. 2018

International Journal of Precision Agricultural Aviation

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Detection of crop heights by UAVs is fast and accurate and can reflect the growth situation of crops. The core of this operation is the measurement of UAV flight altitude. In order to improve the accuracy of the response to the variation of flight altitude, a light-weight altitude detection system was developed for plant-protection UAVs. A millimeter wave radar (MWR) was used as the altitude detector. Moreover, a data fusion algorithm based on the Adaptive Kalman Filter (AKF) was developed. The altitude data by the MWR, the angle data by the Inertial Measurement Unit (IMU) and the position data by Global Positioning System (GPS) were processed by the AKF to obtain optimal updated values, and the optimal updated values was fused to establish the distribution map of crop heights. Results of the trials in the condition of the flight heights of 5 m and 10 m indicated that: 1) compared with the direct detection by the MWR, the error of detection was reduced by 0.035 m.2) compared with real crop heights, the error of detection was 0.02 m. The developed system could achieve the accurate detection of the crop height, providing a new theoretical model and technical idea for the UAVs configured for plant-protection.

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A Height Estimation Approach for Terrain Following Flights from Monocular Vision

Cited by 13 publications

References 11 publications

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Detection of crop heights by UAVs based on the Adaptive Kalman Filter

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