Landmark database selection for vision-aided inertial navigation in planetary landing missions

Xu, Chao; Huang, Xiang‐Yu; Li, Maodeng; Wang, Dayi

doi:10.1016/j.ast.2021.107040

Cited by 10 publications

(2 citation statements)

References 34 publications

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“…Especially, the SLAM builds a scene map and match it to ensure the trajectory without drifting over time. However, the visual methods for absolute position estimation are also generally high computational cost and require large memory [5].…”

Section: Introductionmentioning

confidence: 99%

An integrated position estimation by using optical flow for visual robot navigation

Pan,

Tan,

2024

MIPPR 2023: Automatic Target Recognition and Navigation

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A visual integrated position estimation by using simple Optical Flow (OF) is presented for visual robot’s precise navigation. This position estimation consists of a relative and an absolute position estimation. In the relative position estimation, an OF method inspired by honeybee-vision is improved to obtain robot’s relative position. It can measure robot’s translation precisely with regardless of robot’s rotation, while its position errors will be accumulated unavoidably. In the absolute position estimation, an OF-based Kalman Filter (KF) is used to iteratively correct robot’s position errors according the actual and the predicted OF, so the accumulated errors within the relative position estimation is eliminated. As the OF method and the OF-based KF adopt the same method to measure the OF information, the relative position and the absolute position estimation can share input signal and some executive stages for timesaving and costsaving. The experiments using a visual mobile robot in workshop have demonstrated the proposal’s efficiency.

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Section: Introductionmentioning

confidence: 99%

An integrated position estimation by using optical flow for visual robot navigation

Pan,

Tan,

2024

MIPPR 2023: Automatic Target Recognition and Navigation

View full text Add to dashboard Cite

show abstract

“…The AI-IP will be able of processing images acquired by a navigation camera, generating pseudo-measurements that can be exploited by a sensor-fusion filter, to retrieve the estimated state. In particular, literature demonstrates that craters database matching is typically exploited to retrieve the absolute pseudo-measurement [10,11]. In these papers, the database matching is performed via nearest neighbor approach, linking the output from the object detector to the navigation filter.…”

Section: Introductionmentioning

confidence: 99%