Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach
This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirements of fast exploration and high resolution are solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so tha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/24/9807 |
| _version_ | 1797455298060877824 |
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| author | Barak Pinkovich Boaz Matalon Ehud Rivlin Hector Rotstein |
| author_facet | Barak Pinkovich Boaz Matalon Ehud Rivlin Hector Rotstein |
| author_sort | Barak Pinkovich |
| collection | DOAJ |
| description | This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirements of fast exploration and high resolution are solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that the spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of the patches becomes larger than a prespecified threshold, suitability for landing is declared. One of the main building blocks of the approach is a semantic segmentation algorithm that attaches probabilities to each pixel of a single view. The decision algorithm combines these probabilities with a priori data and previous measurements to obtain the best estimates. Feasibility is illustrated by presenting several examples generated by a realistic closed-loop simulator. |
| first_indexed | 2024-03-09T15:52:25Z |
| format | Article |
| id | doaj.art-40e8b8894bd9489887796226aad535e8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:25Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-40e8b8894bd9489887796226aad535e82023-11-24T17:55:38ZengMDPI AGSensors1424-82202022-12-012224980710.3390/s22249807Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic ApproachBarak Pinkovich0Boaz Matalon1Ehud Rivlin2Hector Rotstein3The Faculty of Computer Science, Technion Israel Institute of Technology, Haifa 3200003, IsraelRafael Advanced Defense Systems Ltd., Haifa 3102102, IsraelThe Faculty of Computer Science, Technion Israel Institute of Technology, Haifa 3200003, IsraelThe Faculty of Computer Science, Technion Israel Institute of Technology, Haifa 3200003, IsraelThis paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirements of fast exploration and high resolution are solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that the spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of the patches becomes larger than a prespecified threshold, suitability for landing is declared. One of the main building blocks of the approach is a semantic segmentation algorithm that attaches probabilities to each pixel of a single view. The decision algorithm combines these probabilities with a priori data and previous measurements to obtain the best estimates. Feasibility is illustrated by presenting several examples generated by a realistic closed-loop simulator.https://www.mdpi.com/1424-8220/22/24/9807unmanned aerial vehiclessearch theoryperceptionsemantic segmentation |
| spellingShingle | Barak Pinkovich Boaz Matalon Ehud Rivlin Hector Rotstein Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach Sensors unmanned aerial vehicles search theory perception semantic segmentation |
| title | Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach |
| title_full | Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach |
| title_fullStr | Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach |
| title_full_unstemmed | Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach |
| title_short | Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach |
| title_sort | finding a landing site in an urban area a multi resolution probabilistic approach |
| topic | unmanned aerial vehicles search theory perception semantic segmentation |
| url | https://www.mdpi.com/1424-8220/22/24/9807 |
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