Seeker Gyro Calibration Via Model-Based Fusion of Visual and Inertial Data

Gol'dshtein, M. N.; Oshman, Yaakov; Efrati, T.

doi:10.2514/6.2008-7424

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…The equations of motion given by Eqs. (12)(13)(14)(15) are integrated forward in time using a fixed time step Runge-Kutta integration routine to obtain a single flight trajectory. Note that control action is input to this system through specification of the roll angle ϕ C , which describes the maneuver plane in which the wing actuator is fully extended.…”

Section: B Flight Dynamic Modelmentioning

confidence: 99%

“…The pinhole camera is used to project a three-dimensional scene onto a twodimensional image plane, neglecting distortion effects from the lens or finite aperture size [11]. As mentioned previously, a pinhole camera model may be used to represent both seekers and standard camera devices [12]. Thus, details concerning whether detector data arrive from a laser-designated spot detector or through image-based navigation are irrelevant for this study and it is assumed that the target is identifiable in the detector plane.…”

Section: Detector Modelingmentioning

confidence: 99%

“…where the left-hand side of Eq. (18) represents the raw displacements of the target in the image plane and η y , η z are zero-mean Gaussian white noise representing detector quantization error, distortion effects, and other sources of detector noise [12]. A diagram of the projective transformation for ϵ z is shown in Fig.…”

Section: Detector Modelingmentioning

confidence: 99%

“…It is assumed that the imager used in this study may be modeled as a pinhole camera [11]. Thus, details concerning whether the strapdown (or "stiff-neck") detector signals are derived from a laser-designated spot or through image-based navigation are irrelevant for this study, because both seekers and standard cameras approximately satisfy the pinhole camera modeling assumptions [12]. The control system presented here is applied to an example 40 mm grenade projectile equipped with a rotating wing maneuver mechanism.…”

mentioning

confidence: 99%

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Flight Control of a Small-Diameter Spin-Stabilized Projectile Using Imager Feedback

Fresconi

Rogers

2015

Journal of Guidance, Control, and Dynamics

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Small-diameter gun-launched projectiles pose a challenging platform on which to implement closed-loop guidance and control. This paper presents a novel imager-based guidance and control algorithm for small-diameter spinstabilized projectiles. The control law is specifically formulated to rely on feedback only from a strapdown detector and roll angle sensors. Following introduction of the projectile nonlinear dynamic model, an integrated guidance and control algorithm is presented in which control commands are computed directly from detector feedback using a gainscheduled proportional control. Time-varying controller gains are derived through a surrogate modeling approach, and controller performance is further enhanced through use of an observer that filters unwanted angular motion components from detector feedback. Example closed-loop flight simulations demonstrate performance of the proposed control system, and Monte Carlo analysis shows a factor of 2 accuracy improvement for the closed-loop system over ballistic flight. Results indicate that delivery system improvements are achievable in small, gyroscopically stabilized projectiles containing low-cost guidance elements using the proposed integrated guidance and control approach.

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Section: B Flight Dynamic Modelmentioning

confidence: 99%

Section: Detector Modelingmentioning

confidence: 99%

Section: Detector Modelingmentioning

confidence: 99%

mentioning

confidence: 99%

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Flight Control of a Small-Diameter Spin-Stabilized Projectile Using Imager Feedback

Fresconi

Rogers

2015

Journal of Guidance, Control, and Dynamics

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“…To overcome these problems, some studies have attempted to combine GPS with other sensors such as INS and cameras (Langel Steven et al, 2010;Andrey and Donald, 2010;Yoo et al, 2005). Especially, hybrid approaches with additional cameras seem to be promising since many cars tend to have built-in front and around view monitoring cameras (Andrey and Donald, 2010;Kim et al, 2011;Yoo et al, 2005;Madison et al, 2007;Nettleton and Sukkarieh, 2004;Goldshtein et al, 2007;Kim et al, 2012). For example, extracted lanes from images and estimated displacement to correct GPS estimation, but this approach has limitation under the situation where the number of visible satellites is not enough for a long time or the lane detection are difficult.…”

Section: Introductionmentioning

confidence: 99%

Realtime Image Matching for Vision Based Car Navigation with Built-in Sensory Data

Choi

Tanathong

Kim

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Recently, a car employs various built-in sensors such as a speedometer, odometer, accelerometer and angular rate sensor for safety and maintenance. These sensory data can be provided in real time through a CAN (Controller Area Network) bus. In addition, image sequences can be provided from various cameras mounted to the car, such as built-in front and around view monitoring cameras. We thus propose an image based car navigation framework to determine car position and attitude using the built-in sensory data such as a speed, angular rate and images from a front view camera. First, we determine the two-dimensional position and attitude of a car using the velocity and angular rate provided in real-time through the CAN bus. We then estimate the three-dimensional position and attitude by conducting sequential bundle block adjustment using the two-dimensional position and attitude and tie points between image sequences. The sequential bundle adjustment can produce accurate results comparable to those from the conventional simultaneous bundle adjustment in real time. As the input to this process, it needs reliable tie points between adjacent images acquired from a real-time image matching process. Hence, we develop an image matching process based on the enhanced KLT algorithm using preliminary exterior orientation parameters. We also construct a test system that can acquire and store built-in sensory data and front camera images at the same time, and conduct experiments with the real data acquired by the system. The experimental results show that the proposed image matching process can generate accurate tie-points with about 0.2 second in average at each epoch. It can successfully meet the requirements from real-time bundle adjustment for image based car navigation.

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Observability analysis of inertial navigation errors from optical flow subspace constraint

Waldmann

Silva

Chagas

2016

Information Sciences

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Seeker Gyro Calibration Via Model-Based Fusion of Visual and Inertial Data

Cited by 4 publications

References 17 publications

Flight Control of a Small-Diameter Spin-Stabilized Projectile Using Imager Feedback

Flight Control of a Small-Diameter Spin-Stabilized Projectile Using Imager Feedback

Realtime Image Matching for Vision Based Car Navigation with Built-in Sensory Data

Observability analysis of inertial navigation errors from optical flow subspace constraint

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