Representing Substantial Heading Uncertainty for Accurate Geolocation by Small UAVs

Nuske, Stephen; Dille, Michael; Grocholsky, Ben; Singh, Sanjiv

doi:10.2514/6.2010-7886

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Focusing on the uncertainty in the attitude parameters, it is important to highlight that the heading, pitch and roll estimates do not have the same accuracy level. Specifically, due to the possibility to exploit gravity direction, considering performance of low-cost IMUs on board of small UAVs, the uncertainties in the roll and pitch estimates provided by the onboard autopilot are typically bounded at the degree-level [63], while the error in the heading estimate can be significantly larger (up to several degrees) [64]. For this reason, the target search can be limited to a horizontal stripe in the image plane.…”

Section: Detection and Tracking Architecturementioning

confidence: 99%

Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning

Opromolla

Inchingolo

Fasano

2019

Sensors

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The performance achievable by using Unmanned Aerial Vehicles (UAVs) for a large variety of civil and military applications, as well as the extent of applicable mission scenarios, can significantly benefit from the exploitation of formations of vehicles able to fly in a coordinated manner (swarms). In this respect, visual cameras represent a key instrument to enable coordination by giving each UAV the capability to visually monitor the other members of the formation. Hence, a related technological challenge is the development of robust solutions to detect and track cooperative targets through a sequence of frames. In this framework, this paper proposes an innovative approach to carry out this task based on deep learning. Specifically, the You Only Look Once (YOLO) object detection system is integrated within an original processing architecture in which the machine-vision algorithms are aided by navigation hints available thanks to the cooperative nature of the formation. An experimental flight test campaign, involving formations of two multirotor UAVs, is conducted to collect a database of images suitable to assess the performance of the proposed approach. Results demonstrate high-level accuracy, and robustness against challenging conditions in terms of illumination, background and target-range variability.

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Section: Detection and Tracking Architecturementioning

confidence: 99%

Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning

Opromolla

Inchingolo

Fasano

2019

Sensors

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“…For instance, multi-antenna-based GNSS techniques allow reaching sub-degree level in the attitude accuracy [17], but they pose installation constraints which may not be met considering the allocation resources of many micro/small UAVs [19]. So, in the most general case, considering performance of low-cost IMUs on board MAV, the uncertainties in the roll and pitch estimates provided by the onboard autopilot are typically bounded at the degree-level [20] (due to the presence of gravity), while the error in the heading angle estimate can be up to several degrees [21].…”

Section: Introductionmentioning

confidence: 99%

Magnetometer Calibration for Small Unmanned Aerial Vehicles Using Cooperative Flight Data

Opromolla

2020

Sensors

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This paper presents a new method to improve the accuracy in the heading angle estimate provided by low-cost magnetometers on board of small Unmanned Aerial Vehicles (UAVs). This task can be achieved by estimating the systematic error produced by the magnetic fields generated by onboard electric equipment. To this aim, calibration data must be collected in flight when, for instance, the level of thrust provided by the electric engines (and, consequently, the associated magnetic disturbance) is the same as the one occurring during nominal flight operations. The UAV whose magnetometers need to be calibrated (chief) must be able to detect and track a cooperative vehicle (deputy) using a visual camera, while flying under nominal GNSS coverage to enable relative positioning. The magnetic biases’ determination problem can be formulated as a system of non-linear equations by exploiting the acquired visual and GNSS data. The calibration can be carried out either off-line, using the data collected in flight (as done in this paper), or directly on board, i.e., in real time. Clearly, in the latter case, the two UAVs should rely on a communication link to exchange navigation data. Performance assessment is carried out by conducting multiple experimental flight tests.

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“…The authors previously presented [15] a sampling-based evidence grid filter that makes no assumptions about the form of observation or a-posteriori uncertainty distributions and provides accurate estimates in the presence of large, nonGaussian, and time-varying errors in vehicle state, particularly heading. However, it is not easily extended to moving targets, it provides only a very conservative measure of estimate confidence, and while it runs in real-time for grid sizes of several meters and sampling along several state axes, increasing grid resolution and sampling along many additional axes could result in impractical runtimes.…”

Section: Related Workmentioning

confidence: 99%

Efficient target geolocation by highly uncertain small air vehicles

Grocholsky

Dille

Nuske

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Geolocation of a ground object or target of interest from live video is a common task required of small and micro unmanned aerial vehicles (SUAVs and MAVs) in surveillance and rescue applications. However, such vehicles commonly carry low-cost and light-weight sensors providing poor bandwidth and accuracy whose contribution to observations is nonlinear, resulting in poor geolocation performance by standard techniques. This paper proposes the application of an efficient over-parameterized state representation to the problem of geolocation that is able to handle large, time-varying, and non-Gaussian sensor error to produce better geolocation estimates than typical approaches and which provides computing and communication benefits in applications such as predictive control and distributed collaboration. We evaluate our filter on real flight data, demonstrating its ability to efficiently produce a solution with tight confidence bounds given highly uncertain data.

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Representing Substantial Heading Uncertainty for Accurate Geolocation by Small UAVs

Cited by 8 publications

References 12 publications

Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning

Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning

Magnetometer Calibration for Small Unmanned Aerial Vehicles Using Cooperative Flight Data

Efficient target geolocation by highly uncertain small air vehicles

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