Tracking control of a UAV with a parallel visual processor

Greatwood, Colin; Bose, Laurie; Richardson, Thomas; Mayol-Cuevas, Walterio; Chen, Jianing; Carey, Stephen J.; Dudek, Piotr

doi:10.1109/iros.2017.8206286

Cited by 27 publications

(21 citation statements)

References 18 publications

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“…The multirotor followed the same predefined path used to test the visual odometry in isolation however, a target shown in the final frame of Figure 5 approximately 1m in diameter was placed on the ground by one corner of the flight path. This target extraction was previously integrated into the control loop of a multirotor to track and follow a moving target (Greatwood et al, 2017 ). Figure 9 shows a flight path with the region where the target is identified in parallel with the VIO track being estimated.…”

Section: Resultsmentioning

confidence: 99%

“…This section briefly describes the visual odometry algorithm executed upon the SCAMP-5 sensor, based upon the previous work of Bose et al ( 2017 ), along with the algorithm used to identify and extract a specific target pattern as previously used in Greatwood et al ( 2017 ).…”

Section: Methodsmentioning

confidence: 99%

“…The same flight path from the initial odometry tests were used, however a target as shown in the final frame of Figure 5 is placed by one corner of the path as shown in Figure 6 . The purpose of this test is to both demonstrate the capability of the work done by Greatwood et al ( 2017 ), and demonstrate multiple algorithms running on-board the SCAMP-5. We correct the position of the odometry when the target is detected, using for demonstration purposes an assumed target position and zero error in the distance estimation upon detection.…”

Section: Methodsmentioning

confidence: 99%

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Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

et al. 2020

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Environments in which Global Positioning Systems (GPS), or more generally Global Navigation Satellite System (GNSS), signals are denied or degraded pose problems for the guidance, navigation, and control of autonomous systems. This can make operating in hostile GNSS-Impaired environments, such as indoors, or in urban and natural canyons, impossible or extremely difficult. Pixel Processor Array (PPA) cameras-in conjunction with other on-board sensors-can be used to address this problem, aiding in tracking, localization, and control. In this paper we demonstrate the use of a PPA device-the SCAMP vision chip-combining perception and compute capabilities on the same device for aiding in real-time navigation and control of aerial robots. A PPA consists of an array of Processing Elements (PEs), each of which features light capture, processing, and storage capabilities. This allows various image processing tasks to be efficiently performed directly on the sensor itself. Within this paper we demonstrate visual odometry and target identification running concurrently on-board a single PPA vision chip at a combined frequency in the region of 400 Hz. Results from outdoor multirotor test flights are given along with comparisons against baseline GPS results. The SCAMP PPA's High Dynamic Range (HDR) and ability to run multiple algorithms at adaptive rates makes the sensor well suited for addressing outdoor flight of small UAS in GNSS challenging or denied environments. HDR allows operation to continue during the transition from indoor to outdoor environments, and in other situations where there are significant variations in light levels. Additionally, the PPA only needs to output specific information such as the optic flow and target position, rather than having to output entire images. This significantly reduces the bandwidth required for communication between the sensor and on-board flight computer, enabling high frame rate, low power operation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

et al. 2020

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“…By improving the vector field guidance method of a single UAV and defining a variable confrontation tracking track, the cooperative confrontation tracking of the UAV group on a moving target group is used to solve the problem of the visual range of the UAV when tracking multiple ground targets, which is suitable for processing aerial video images of a fast-moving target. To solve the problem of visual control of target tracking in visible light aerial photography, Reference [ 51 ] adopted a ground target tracking control strategy based on vision to realize the real-time tracking of aerial photography targets. Aiming at solving the regional cooperative search problem of multi-UAVs, Reference [ 52 ] described the changes in the environment and target state with the search process based on the search information graph model and established a motion model for the dynamic analysis of UAVs to ensure the accuracy of model prediction, thereby realizing the accurate tracking of complex targets with motion trajectories.…”

Section: Traditional Target Tracking Algorithmmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

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“…High dynamic range means that the vision sensor can perform in a wide variety of demanding lighting conditions that might otherwise saturate other systems. It is also possible to reprogram the PPA device for other tasks, such as target tracking [14], with no need to carry additional computer hardware for visual processing. Given this flexibility, future work could investigate the image matching techniques used here for localisation.…”

Section: High Dynamic Rangementioning

confidence: 99%

Perspective Correcting Visual Odometry for Agile MAVs using a Pixel Processor Array

Greatwood

Bose

Richardson

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a visual odometry approach using a Pixel Processor Array (PPA) camera, specifically, the SCAMP-5 vision chip. In this device, each pixel is capable of storing data and performing computation, enabling a variety of computer vision tasks to be carried out directly upon the sensor itself. In this work the PPA performs HDR edge detection, perspective correction and image alignment based odometry, allowing the position and heading of a MAV to be tracked at several hundred frames per second. We evaluate our PPA based approach by direct comparison with a motion capture system for a variety of trajectories. These include rapid accelerations that would incur significant motion blur at low frame rates, and lighting conditions that would typically lead to under or over exposure of image detail. Such challenging conditions would often lead to unusable images when relying on traditional image sensors.

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Tracking control of a UAV with a parallel visual processor

Cited by 27 publications

References 18 publications

Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

Aerial Video Trackers Review

Perspective Correcting Visual Odometry for Agile MAVs using a Pixel Processor Array

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