Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. Howe...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/3/1151 |
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| Summary: | Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets—the public S3DIS dataset and the self-collected HoloLens 2 dataset—and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response. |
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| ISSN: | 2076-3417 |