Vision-Aided Multi-UAV Autonomous Flocking in GPS-Denied Environment

Tang, Yazhe; Hu, Yuchao; Cui, Jinqiang; Liao, Fang; Lao, Mingjie; Lin, Feng; Teo, Rodney

doi:10.1109/tie.2018.2824766

Cited by 130 publications

(50 citation statements)

References 26 publications

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“…It is possible to observe that the camera positioning (magenta dots) converges to the reference (blue dots) in Figure 7a for each studied height. In terms of the root medium squared error (RMSE), our approach was within this tolerance limit for this type of applications [57,58]. Furthermore, The algorithm accuracy was higher than 90.35% in the three-phase system detection, as shown in Table 2a.…”

Section: Laboratory Validationmentioning

confidence: 62%

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

2019

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Unmanned aerial vehicles (UAVs) are an emerging and promising alternative for monitoring of transmission lines in terms of flexibility, complexity, working speed, and cost. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the UAV placement with respect to the transmission grid. This work faces this challenge by developing a transmission line autonomous tracking system, which allows the placement of a commercial drone over a transmission grid using a monocular camera. This feature provides accurate positioning for the vehicle even where the Global navigation satellite system (GNSS) signal is denied, enabling to report the status of transmission lines, at any time. The system isolates transmission grid conductors in each acquired RGB-image using an image-processing algorithm based on Hough transform, morphological operations, and Gabor filters. With this information, the system computes the location of the UAV using a geometric approach that relates transmission lines building parameter and optical geometry. However, it has the problem of gradual error accumulation when the drone moves. In this regards, the estimated position of the drone is computed by the maximum likelihood estimation (MLE) by the position information estimated by visual-system, the inertial measurement unit (IMU) and GNSS. The proposed positioning system showed an efficiency of 91.44% in field experimentation in the extraction of transmission conductor, with a root mean square the error of 0.18 m in the UAV localization.

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Section: Laboratory Validationmentioning

confidence: 62%

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

2019

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“…excavators, bulldozers, wood processors). Other obvious potential application areas are drones and robots [9]- [11]. One successful realization architecture uses a multi-channel spinning radar unit that can simultaneously measure distances on several (e.g., 64) vertical planes [9], [10].…”

Section: Introductionmentioning

confidence: 99%

A CMOS Receiver–TDC Chip Set for Accurate Pulsed TOF Laser Ranging

Kurtti

Jansson

Kostamovaara

2020

IEEE Trans. Instrum. Meas.

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An integrated receiver-TDC (time-to-digital converter) chip set is developed for pulsed time-of-flight (TOF) laser rangefinding. The receiver detects the current pulse from the optical detector and produces a timing mark for the TDC. The receiver uses time mode walk error compensation scheme achieving <+/-2.5 mm residual timing walk error within a dynamic range of ~1:40,000. The multi-channel TDC measures the time position, width and rise time of the echo pulses simultaneously with ~ 10 picosecond (ps) precision. Both chips are manufactured in 0.35-µm complementary metal-oxide semiconductor (CMOS) technology. The functionality of the chip set was demonstrated in a laser radar platform using 12 W and 3-ns optical pulses produced by a laser diode. A measurement accuracy of ~+/-3 mm was achieved with non-cooperative targets at a distance range of a few tens of metres within an amplitude range of received echoes of 1:40,000.

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“…In order to complete navigation under the circumstance where the GNSS signal is lost, many international strategies have been proposed, including GPS/INS integrated navigation [16][17][18], anti-interference design of GNSS equipment [19], and additional auxiliary sensors such as optical flow [20], radar [21,22], and other sensors for navigation assistance. With the development of machine vision systems in recent years, it has become possible to correct the flight path through machine vision [23][24][25][26][27][28][29][30][31][32][33][34]. With the vision system, UAV positioning navigation can completely get rid of the external positioning information, and stand-alone navigation can be achieved through the sensing equipment and camera that it carries.…”

Section: Introductionmentioning

confidence: 99%

Obtaining World Coordinate Information of UAV in GNSS Denied Environments

Chen

Tian

Liang

et al. 2020

Sensors

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GNSS information is vulnerable to external interference and causes failure when unmanned aerial vehicles (UAVs) are in a fully autonomous flight in complex environments such as high-rise parks and dense forests. This paper presents a pan-tilt-based visual servoing (PBVS) method for obtaining world coordinate information. The system is equipped with an inertial measurement unit (IMU), an air pressure sensor, a magnetometer, and a pan-tilt-zoom (PTZ) camera. In this paper, we explain the physical model and the application method of the PBVS system, which can be briefly summarized as follows. We track the operation target with a UAV carrying a camera and output the information about the UAV’s position and the angle between the PTZ and the anchor point. In this way, we can obtain the current absolute position information of the UAV with its absolute altitude collected by the height sensing unit and absolute geographic coordinate information and altitude information of the tracked target. We set up an actual UAV experimental environment. To meet the calculation requirements, some sensor data will be sent to the cloud through the network. Through the field tests, it can be concluded that the systematic deviation of the overall solution is less than the error of GNSS sensor equipment, and it can provide navigation coordinate information for the UAV in complex environments. Compared with traditional visual navigation systems, our scheme has the advantage of obtaining absolute, continuous, accurate, and efficient navigation information at a short distance (within 15 m from the target). This system can be used in scenarios that require autonomous cruise, such as self-powered inspections of UAVs, patrols in parks, etc.

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Vision-Aided Multi-UAV Autonomous Flocking in GPS-Denied Environment

Cited by 130 publications

References 26 publications

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

A CMOS Receiver–TDC Chip Set for Accurate Pulsed TOF Laser Ranging

Obtaining World Coordinate Information of UAV in GNSS Denied Environments

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