Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309323742> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W4309323742 endingPage "12561" @default.
- W4309323742 startingPage "12555" @default.
- W4309323742 abstract "The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation." @default.
- W4309323742 created "2022-11-26" @default.
- W4309323742 creator A5019551456 @default.
- W4309323742 creator A5028329562 @default.
- W4309323742 creator A5069766954 @default.
- W4309323742 creator A5090408011 @default.
- W4309323742 date "2022-10-01" @default.
- W4309323742 modified "2023-10-17" @default.
- W4309323742 title "Hardware-Accelerated Mars Sample Localization Via Deep Transfer Learning From Photorealistic Simulations" @default.
- W4309323742 cites W2168676389 @default.
- W4309323742 cites W2314931868 @default.
- W4309323742 cites W2565639579 @default.
- W4309323742 cites W2625092762 @default.
- W4309323742 cites W2767011576 @default.
- W4309323742 cites W2811212391 @default.
- W4309323742 cites W2887280559 @default.
- W4309323742 cites W2952390384 @default.
- W4309323742 cites W2954996726 @default.
- W4309323742 cites W2960833983 @default.
- W4309323742 cites W3007493000 @default.
- W4309323742 cites W3009942643 @default.
- W4309323742 cites W3034450565 @default.
- W4309323742 cites W3080475915 @default.
- W4309323742 cites W3110256918 @default.
- W4309323742 cites W3138567652 @default.
- W4309323742 cites W3140854437 @default.
- W4309323742 cites W3167004715 @default.
- W4309323742 cites W3188101290 @default.
- W4309323742 cites W4205775512 @default.
- W4309323742 doi "https://doi.org/10.1109/lra.2022.3219306" @default.
- W4309323742 hasPublicationYear "2022" @default.
- W4309323742 type Work @default.
- W4309323742 citedByCount "1" @default.
- W4309323742 countsByYear W43093237422023 @default.
- W4309323742 crossrefType "journal-article" @default.
- W4309323742 hasAuthorship W4309323742A5019551456 @default.
- W4309323742 hasAuthorship W4309323742A5028329562 @default.
- W4309323742 hasAuthorship W4309323742A5069766954 @default.
- W4309323742 hasAuthorship W4309323742A5090408011 @default.
- W4309323742 hasBestOaLocation W43093237422 @default.
- W4309323742 hasConcept C121332964 @default.
- W4309323742 hasConcept C127313418 @default.
- W4309323742 hasConcept C154945302 @default.
- W4309323742 hasConcept C185592680 @default.
- W4309323742 hasConcept C198531522 @default.
- W4309323742 hasConcept C2776219924 @default.
- W4309323742 hasConcept C2778600265 @default.
- W4309323742 hasConcept C31258907 @default.
- W4309323742 hasConcept C31395832 @default.
- W4309323742 hasConcept C41008148 @default.
- W4309323742 hasConcept C43617362 @default.
- W4309323742 hasConcept C62649853 @default.
- W4309323742 hasConcept C68702407 @default.
- W4309323742 hasConcept C78949437 @default.
- W4309323742 hasConcept C83260615 @default.
- W4309323742 hasConcept C87355193 @default.
- W4309323742 hasConceptScore W4309323742C121332964 @default.
- W4309323742 hasConceptScore W4309323742C127313418 @default.
- W4309323742 hasConceptScore W4309323742C154945302 @default.
- W4309323742 hasConceptScore W4309323742C185592680 @default.
- W4309323742 hasConceptScore W4309323742C198531522 @default.
- W4309323742 hasConceptScore W4309323742C2776219924 @default.
- W4309323742 hasConceptScore W4309323742C2778600265 @default.
- W4309323742 hasConceptScore W4309323742C31258907 @default.
- W4309323742 hasConceptScore W4309323742C31395832 @default.
- W4309323742 hasConceptScore W4309323742C41008148 @default.
- W4309323742 hasConceptScore W4309323742C43617362 @default.
- W4309323742 hasConceptScore W4309323742C62649853 @default.
- W4309323742 hasConceptScore W4309323742C68702407 @default.
- W4309323742 hasConceptScore W4309323742C78949437 @default.
- W4309323742 hasConceptScore W4309323742C83260615 @default.
- W4309323742 hasConceptScore W4309323742C87355193 @default.
- W4309323742 hasIssue "4" @default.
- W4309323742 hasLocation W43093237421 @default.
- W4309323742 hasLocation W43093237422 @default.
- W4309323742 hasOpenAccess W4309323742 @default.
- W4309323742 hasPrimaryLocation W43093237421 @default.
- W4309323742 hasRelatedWork W1574919219 @default.
- W4309323742 hasRelatedWork W2123245438 @default.
- W4309323742 hasRelatedWork W265524408 @default.
- W4309323742 hasRelatedWork W3033770388 @default.
- W4309323742 hasRelatedWork W3154693760 @default.
- W4309323742 hasRelatedWork W4292622938 @default.
- W4309323742 hasRelatedWork W4309323742 @default.
- W4309323742 hasRelatedWork W44886576 @default.
- W4309323742 hasRelatedWork W773373010 @default.
- W4309323742 hasRelatedWork W780354523 @default.
- W4309323742 hasVolume "7" @default.
- W4309323742 isParatext "false" @default.
- W4309323742 isRetracted "false" @default.
- W4309323742 workType "article" @default.