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Limited environmental information path planning based on 3D point cloud reconstruction

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Abstract

We present a new limited environmental information path planning procedure (IEIPPP) that finds collision-free paths without prior knowledge of feasible paths or obstacle locations by analyzing an image set of the area of interest. IEIPPP uses COLMAP to process the image set and reconstruct a three-dimensional point cloud model of the environment. Then, mechanical selective rapidly exploring random tree star is used to find the required path on the point cloud model. Gravitation and repulsion are introduced to correct the positions of random nodes and reduce the collision probability, and an elastic potential energy calculation is introduced to balance the height difference between adjacent nodes and stabilize vertical fluctuation of the path. To reduce computational cost and running time, a target-based sampling strategy is used to enable selective sampling. We evaluate IEIPPP with different image datasets and show that it can identify a collision-free path without other sensor equipment.

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Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The authors received no specific funding for this work.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, People’s Republic of China

    Hanyu Wang & Ying Li

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, People’s Republic of China

    Hanyu Wang & Ying Li

Authors
  1. Hanyu Wang
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  2. Ying Li
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Contributions

HW and YL contributed to conceptualization. HW contributed to data curation, investigation, methodology, software, validation, and writing—original draft. HW and YL contributed to writing—review and editing.

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Correspondence to Hanyu Wang.

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Wang, H., Li, Y. Limited environmental information path planning based on 3D point cloud reconstruction. J Supercomput 80, 10931–10958 (2024). https://doi.org/10.1007/s11227-023-05858-0

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