&lt;title&gt;Automatic rendezvous and docking system test and evaluation&lt;/title&gt;

Howard, Richard T.; Cole, Helen; Jackson, J.L.; Kamerman, Gary W.; Fronek, Donald K.

doi:10.1117/12.281002

Cited by 6 publications

(2 citation statements)

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“…The initial FP test ran from August 1 1,2003 until September 4 at the following ranges (meters): 1,3.2,4,5,208, and 300. Also different target configurations and sensor alignments were used.…”

Section: Fp Resultsmentioning

confidence: 99%

Advanced Video Guidance Sensor (AVGS) development testing

et al. 2004

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NASA's Marshall Space Flight Center was the driving force behind the development of the Advanced Video Guidance Sensor, an active sensor system that provides near-range sensor data as part of an automatic rendezvous and docking system. The sensor determines the relative positions and attitudes between the active sensor and the passive target at ranges up to 300 meters. The AVGS uses laser diodes to illuminate retro-reflectors in the target, a solid-state camera to detect the return from the target, and image capture electronics and a digital signal processor to convert the video information into the relative positions and attitudes. The AVGS will fly as part of the Demonstration of Autonomous Rendezvous Technologies (DART) in October, 2004. This development effort has required a great deal of testing of various sorts at every phase of development. Some of the test efforts included optical characterization of performance with the intended target, thermal vacuum testing, performance tests in long range vacuum facilities, EMI/EMC tests, and performance testing in dynamic situations. The sensor has been shown to track a target at ranges of up to 300 meters, both in vacuum and ambient conditions, to survive and operate during the thermal vacuum cycling specific to the DART mission, to handle EM1 well, and to perform well in dynamic situations.

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Section: Fp Resultsmentioning

confidence: 99%

Advanced Video Guidance Sensor (AVGS) development testing

et al. 2004

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“…Several studies have investigated pose measurement algorithms, features for pose measurement, 3-D reconstruction, and the test environment for pose measurement [4]- [9]. For instance, [10] and [11] described the composition of the pose measuring system in video guidance sensor (VGS). A physical map of multiple path beyond line of sight communication was introduced in [12] and [13].…”

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confidence: 99%

Precise Measurement of Position and Attitude Based on Convolutional Neural Network and Visual Correspondence Relationship

Yang

Man

Meng

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

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Accurate measurement of position and attitude information is particularly important. Traditional measurement methods generally require high-precision measurement equipment for analysis, leading to high costs and limited applicability. Vision-based measurement schemes need to solve complex visual relationships. With the extensive development of neural networks in related fields, it has become possible to apply them to the object position and attitude. In this paper, we propose an object pose measurement scheme based on convolutional neural network and we have successfully implemented end-toend position and attitude detection. Furthermore, to effectively expand the measurement range and reduce the number of training samples, we demonstrated the independence of objects in each dimension and proposed subadded training programs. At the same time, we generated generating image encoder to guarantee the detection performance of the training model in practical applications.

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