Factor Graph Modeling of Rigid‐body Dynamics for Localization, Mapping, and Parameter Estimation of a Spinning Object in Space

Tweddle, Brent; Saenz-Otero, Alvar; Leonard, John J.; Miller, David W.

doi:10.1002/rob.21548

Cited by 55 publications

(17 citation statements)

References 34 publications

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“…A few approaches such as [40][41][42] track features over time and employ simultaneous localization and mapping or structure-from-motion techniques to estimate the 3-D structure and dynamics of the target object. Pose is again computed by matching observed features to the estimated structure.…”

Section: A Monocular Approachesmentioning

confidence: 99%

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Lentaris¹,

Maragos²,

Stratakos³

et al. 2018

Journal of Aerospace Information Systems

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Vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions, such as active debris removal for remediating the low Earth orbit environment, will rely on novel high-performance avionics to support advanced image processing algorithms with substantial workloads. However, when designing new avionics architectures, constraints relating to the use of electronics in space present great challenges, further exacerbated by the need for significantly faster processing compared to conventional space-grade central processing units. With the long-term goal of designing highperformance embedded computers for space, in this paper, an extended study and tradeoff analysis of a diverse set of computing platforms and architectures (i.e., central processing units, multicore digital signal processors, graphics processing units, and field-programmable gate arrays) are performed with radiation-hardened and commercial offthe-shelf technology. Overall, the study involves more than 30 devices and 10 benchmarks, which are selected after exploring the algorithms and specifications required for vision-based navigation. The present analysis combines literature survey and in-house development/testing to derive a sizable consistent picture of all possible solutions. Among others, the results show that certain 28 nm system-on-chip devices perform faster than space-grade and embedded central processing units by 1-3 orders of magnitude, while consuming less than 10 W. Field-programmable gate array platforms provide the highest performance per watt ratio.

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Section: A Monocular Approachesmentioning

confidence: 99%

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Lentaris¹,

Maragos²,

Stratakos³

et al. 2018

Journal of Aerospace Information Systems

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“…This session used an inspector SPHERES satellite equipped with the VERTIGO Goggles to gather footage of a second tumbling SPHERES satellite. This data set was processed on the ground to create a three‐dimensional (3D) map and to estimate the state (linear and angular position and velocity) and inertial properties of the target satellite; this analysis is presented in Chapter 6 of Tweddle's Ph.D. thesis (Tweddle, ) and a forthcoming paper in the Journal of Field Robotics (Tweddle, Saenz‐Otereo, Leonard, & Miller, ). This test session also tested an algorithm for circumnavigation of the target satellite using only a depth map generated by the stereo cameras (Fourie et al., , ).…”

Section: Summary Of Research Resultsmentioning

confidence: 99%

An Open Research Facility for Vision‐Based Navigation Onboard the International Space Station

Tweddle

Setterfield

Saenz-Otero

et al. 2015

Journal of Field Robotics

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This paper describes the VERTIGO Goggles, a hardware upgrade to the SPHERES satellites that enables visionbased navigation research in the 6 degree-of-freedom, microgravity environment of the International Space Station (ISS). The Goggles include stereo cameras, an embedded x86 computer, a high-speed wireless communications system, and the associated electromechanical and software systems. The Goggles were designed to be a modular, expandable, and upgradable "open research test bed" that have been used for a variety of other experiments by external researchers. In February 2013, the Goggles successfully completed a hardware checkout on the ISS and was used for initial vision-based navigation research. This checkout included a successful camera calibration by an astronaut onboard the ISS. This paper describes the requirements, design, and operation of this test bed as well as the experimental results of its first checkout operations. C 2015 Wiley Periodicals, Inc.

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“…For example, Tweddle's binocular vision method has achieved fast identification of space debris dynamics parameters. The angular velocity estimation error of Tweddle's method is less than 5 deg/s, the normalized principal moment of inertia error is less than 0.35, the inertial principal axis direction error is less than 12.1 deg, and the identification error of morphology is less than 1.14 cm [7,8]. It should be noted that most of the binocular vision methods are within this accuracy range as well (e.g., the normalized principal moments of inertia error for the Peasce method is less than 0.04 [13]).…”

Section: Feasibility Analysis Of Space Harpoonsmentioning

confidence: 99%

“…For the estimation of three-axis tumbling debris' dynamic parameters, the stereovision methods have been widely used. Tweddle et al [7,8] proposed a method to estimate the dynamic parameters of non-cooperative targets based on binocular vision. In this method, aiming at the space proximity operation [9], the rotation of space debris was modeled by the Euler-Poinsot model, and the estimation of parameters was regarded as SLAM.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Parameter Estimation of Large Space Debris Based on Inertial and Visual Data Fusion

et al. 2021

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Most large space debris has large residual angular momentum, and the de-tumbling and capturing operation can easily cause instability and failure of tracking satellites. Therefore, it is necessary to perform real-time dynamic parameter identification of space debris prior to the imminent de-tumbling and capture operation, thus improving the efficiency and success of active debris removal (ADR) missions. A method for identifying dynamic parameters based on the fusion of visual and inertial data is proposed. To obtain the inertial data, the inertial measurement units (IMU) with light markers were fixed on the debris surface by space harpoon, which has been experimentally proven in space, and the binocular vision was placed at the front of a tracking satellite to obtain coordinates of the light markers. A novel method for denoising inertial data is proposed, which will eliminate the interference from the space environment. Furthermore, based on the denoised data and coordinates of the light markers, the mass-center location is estimated. The normalized angular momentum is calculated using the Euler–Poinsot motion characteristics, and all active debris removal parameters are determined. Simulations with Gaussian noise and experiments in the controlled laboratory have been conducted, the results indicate that this method can provide accurate dynamic parameters for the ADR mission.

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Factor Graph Modeling of Rigid‐body Dynamics for Localization, Mapping, and Parameter Estimation of a Spinning Object in Space

Cited by 55 publications

References 34 publications

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

An Open Research Facility for Vision‐Based Navigation Onboard the International Space Station

Dynamic Parameter Estimation of Large Space Debris Based on Inertial and Visual Data Fusion

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