Critical Advancement in Telerobotic Servicing Vision Technology

Miller, K.; Masciarelli, Jim; Rohrschneider, Reuben R.; Gravseth, Ian

doi:10.2514/6.2010-8919

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…Nowadays, range sensors such as stereo vision, [9][10][11] scanning LIDAR, [12][13][14] and three-dimensional (3-D) time-of-flight (ToF) camera (e.g., 3-D flash LIDAR), 15,16 have gained a lot of attention in the computer vision field. However, these range sensors also have some weaknesses in real space application.…”

Section: Introductionmentioning

confidence: 99%

Scale-unambiguous relative pose estimation of space uncooperative targets based on the fusion of three-dimensional time-of-flight camera and monocular camera

Hao

Chen

et al. 2015

Opt. Eng

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Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/24/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Scale-unambiguous relative pose estimation of space uncooperative targets based on the fusion of three-dimensional time-of-flight camera and monocular cameraAbstract. An approach of scale-unambiguous relative pose estimation for space uncooperative targets based on the fusion of low resolution three-dimensional time-of-flight camera and monocular camera is proposed. No a priori knowledge about the targets is assumed. First, a modified range-intensity Markov random field model is presented to quickly reconstruct the range value for each feature point. Second, the scale-ambiguous relative pose estimation algorithm based on extended Kalman filter-unscented Kalman filter-particle filter combination filter is designed in vision simultaneous localization and mapping framework. Third, the overall scale factor estimation approach based on range-intensity fusion image, which takes the feature points' range reconstruction uncertainty as measurement noise, is proposed for the final scale-unambiguous pose estimation. Finally, some simulations demonstrate the validity and capability of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Scale-unambiguous relative pose estimation of space uncooperative targets based on the fusion of three-dimensional time-of-flight camera and monocular camera

Hao

Chen

et al. 2015

Opt. Eng

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“…[10][11][12][13] It can provide both range and intensity information at a video frame rate at a distance. [10][11][12][13] It can provide both range and intensity information at a video frame rate at a distance.…”

Section: Introductionmentioning

confidence: 99%

Dense surface reconstruction based on the fusion of monocular vision and three-dimensional flash light detection and ranging

Hao

Zhao

et al. 2015

Opt. Eng

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A dense surface reconstruction approach based on the fusion of monocular vision and three-dimensional (3-D) flash light detection and ranging (LIDAR) is proposed. The texture and geometry information can be obtained simultaneously and quickly for stationary or moving targets with the proposed method. Primarily, our 2-D/3-D fusion imaging system including cameras calibration and an intensity-range image registration algorithm is designed. Subsequently, the adaptive block intensity-range Markov random field (MRF) with optimizing weights is presented to improve the sparse range data from 3-D flash LIDAR. Then the energy function is minimized quickly by conjugate gradient algorithm for each neighborhood system instead of the whole MRF. Finally, the experiments with standard depth datasets and real 2-D/3-D images demonstrate the validity and capability of the proposed scheme.

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“…This instrument, built by Ball Aerospace & Technologies Corporation (BATC), is a 256x256 Flash LIDAR that operates at an eye-safe wavelength of 1572 nm. 1 A prototype of the VNS was flown aboard the Space Shuttle Endeavour on STS-134 in May-June 2011 as part of the Sensor Test for Orion RelNav Risk Mitigation (STORRM) Development Test Objective (DTO). 2 After this flight test was completed, follow-up ground tests were performed using the STORRM VNS at the LM Space Operations Simulation Center (SOSC) 3,4 in Colorado where the Orion navigation team recreated the close-range portion of the STS-134 rendezvous trajectory.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Orion Vision Navigation Sensor Performance from STS-134 and the Space Operations Simulation Center

Christian

Patangan

Hinkel

et al. 2012

AIAA Guidance, Navigation, and Control Conference

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The Orion Multi-Purpose Crew Vehicle is a new spacecraft being designed by NASA and Lockheed Martin for future crewed exploration missions. The Vision Navigation Sensor is a Flash LIDAR that will be the primary relative navigation sensor for this vehicle. To obtain a better understanding of this sensor's performance, the Orion relative navigation team has performed both flight tests and ground tests. This paper summarizes and compares the performance results from the STS-134 flight test, called the Sensor Test for Orion RelNav Risk Mitigation (STORRM) Development Test Objective, and the ground tests at the Space Operations Simulation Center.

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Critical Advancement in Telerobotic Servicing Vision Technology

Cited by 7 publications

References 3 publications

Scale-unambiguous relative pose estimation of space uncooperative targets based on the fusion of three-dimensional time-of-flight camera and monocular camera

Scale-unambiguous relative pose estimation of space uncooperative targets based on the fusion of three-dimensional time-of-flight camera and monocular camera

Dense surface reconstruction based on the fusion of monocular vision and three-dimensional flash light detection and ranging

Comparison of Orion Vision Navigation Sensor Performance from STS-134 and the Space Operations Simulation Center

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