Optical Flow as a navigation means for UAV

Miller, Alexander B.; Miller, Bruce W.; Popov, A. K.; Stepanyan, Karen

doi:10.1109/anzcc.2018.8606563

Cited by 8 publications

(10 citation statements)

References 19 publications

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“…Optical flow can be used for active navigation such as frontal obstacle avoidance and altitude control in buildings (Miller et al, 2018). Optical flow can also be used passively to collect the current operating state of the aircraft such as pitch and roll rates (Chahl et al, 2005; Honegger et al, 2013), descent angles (Barrows et al, 2002), direction of travel or lateral drift (Franz et al, 2004; Garratt & Chahl, 2003; Rosser & Chahl, 2019), to perform altitude control or automatic landing (Chahl et al 2004; Srinivasan, Zhang, Chahl, et al, 2000).…”

Section: Optical Flow For Navigationmentioning

confidence: 99%

Characteristics of optical flow from aerial thermal imaging, “thermal flow”

Nguyen

Rosser

Perera

et al. 2022

Journal of Field Robotics

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This study explores the utility of optical flow calculated from thermal imaging cameras, "thermal flow," mounted on an aircraft for localization in day and night conditions. Our sensor implementation utilizes a long wave infrared (LWIR) micro sensor to capture sequences of thermal images and an on-board computer to compute an optical flow estimate. We compared the performance of optical flow from the LWIR camera with the output of visible spectrum optical flow sensor.Flights were conducted spanning a 24 h window to explore how thermal flow performs relative to optical flow as the ground heats and cools. Agreement between optical and thermal flow was found during daylight when both sensors were functional. Additionally, thermal flow results were reliable in the middle of the day through to late evening, gradually degrading until shortly after sunrise.

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Section: Optical Flow For Navigationmentioning

confidence: 99%

Characteristics of optical flow from aerial thermal imaging, “thermal flow”

Nguyen

Rosser

Perera

et al. 2022

Journal of Field Robotics

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“…In recent work [46], we realised the idea of the scaling control using the current estimation of the altitude. Generally, it is difficult to evaluate the effect of this approach since the estimate of altitude without the use of other sensors is based just on the OF estimation which depends on the altitude itself.…”

Section: Of Estimation In the Real Flightmentioning

confidence: 99%

“…Generally it is a nontrivial problem which needs consideration shortly, however now one can suggest the empirical algorithm for the averaging scale changing, based on the data which we have at hands. This empirical algorithm works using current height estimation as described in Table 1 [46].…”

Section: Of Estimation In the Real Flightmentioning

confidence: 99%

“…However, what kind of measurements could serve as a sensor for such switching of averaging? In the previous work [46], we examined the switching utilising the altitude estimation. However, at low altitudes all measurements and estimates become unreliable, so we tried to compare the OF velocities computed by L–K algorithm with the OF velocities calculated with the exact formulas and current estimation of the altitude.…”

Section: Of Estimation In the Real Flightmentioning

confidence: 99%

“…However, at low altitudes all measurements and estimates become unreliable, so we tried to compare the OF velocities computed by L–K algorithm with the OF velocities calculated with the exact formulas and current estimation of the altitude. The parameters of the scaling change algorithm are in Table 1 [46]. The corresponding result of the altitude estimation is in Figure 11.…”

Section: Of Estimation In the Real Flightmentioning

confidence: 99%

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UAV Landing Based on the Optical Flow Videonavigation

Miller

Popov

et al. 2019

Sensors

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An automatic landing of an unmanned aerial vehicle (UAV) is a non-trivial task requiringa solution of a variety of technical and computational problems. The most important is the precisedetermination of altitude, especially at the final stage of approaching to the earth. With currentaltimeters, the magnitude of measurement errors at the final phase of the descent may be unacceptablyhigh for constructing an algorithm for controlling the landing manoeuvre. Therefore, it is desirableto have an additional sensor, which makes possible to estimate the height above the surface of therunway. It is possible to estimate all linear and angular UAV velocities simultaneously with thehelp of so-called optical flow (OF), determined by the sequence of images recorded by an onboardcamera, however in pixel scale. To transform them into the real metrical values it is necessary toknow the current flight altitude and the camera angular position values. The critical feature of theOF is its susceptibility to the camera resolution and the shift rate of the observed scene. During thedescent phase of flight, these parameters change at least one hundred times together with the altitude.Therefore, for reliable application of the OF one needs to coordinate the shooting parameters withthe current altitude. However, in case of the altimeter fault presence, the altitude is also still to beestimated with the aid of the OF, so one needs to have another tool for the camera control. One of thepossible and straightforward ways is the camera resolution change by pixels averaging in computerpart which performed in coordination with theoretically estimated and measured OF velocity. Thearticle presents results of such algorithms testing from real video sequences obtained in flights withdifferent approaches to the runway with simultaneous recording of telemetry and video data.

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Neural network‐based optical flow versus traditional optical flow techniques with thermal aerial imaging in real‐world settings

Nguyen

Rosser

Perera

et al. 2023

Journal of Field Robotics

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The study explores the feasibility of optical flow‐based neural network from real‐world thermal aerial imagery. While traditional optical flow techniques have shown adequate performance, sparse techniques do not work well during cold‐soaked low‐contrast conditions, and dense algorithms are more accurate in low‐contrast conditions but suffer from the aperture problem in some scenes. On the other hand, optical flow from convolutional neural networks has demonstrated good performance with strong generalization from several synthetic public data set benchmarks. Ground truth was generated from real‐world thermal data estimated with traditional dense optical flow techniques. The state‐of‐the‐art Recurrent All‐Pairs Field Transform for the Optical Flow model was trained with both color synthetic data and the captured real‐world thermal data across various thermal contrast conditions. The results showed strong performance of the deep‐learning network against established sparse and dense optical flow techniques in various environments and weather conditions, at the cost of higher computational demand.

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Optical Flow as a navigation means for UAV

Cited by 8 publications

References 19 publications

Characteristics of optical flow from aerial thermal imaging, “thermal flow”

Characteristics of optical flow from aerial thermal imaging, “thermal flow”

UAV Landing Based on the Optical Flow Videonavigation

Neural network‐based optical flow versus traditional optical flow techniques with thermal aerial imaging in real‐world settings

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