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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2018> 27(1): 92-102
Zheng Rong, Shun'an Zhong, Nathan Michael. Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(1): 92-102. doi: 10.15918/j.jbit1004-0579.201827.0112
Citation: Zheng Rong, Shun'an Zhong, Nathan Michael. Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(1): 92-102.doi:10.15918/j.jbit1004-0579.201827.0112
shu

Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis

doi:10.15918/j.jbit1004-0579.201827.0112
  • 1.

    School of Information and Electronics, Beijing Instituteof Technology, Beijing 100081, China;Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    School of Information and Electronics, Beijing Instituteof Technology, Beijing 100081, China

  • 3.

    Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA

  • Received Date:2017-03-08
    Abstract
  • An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated, and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimization-based monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.
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