A Robust Approach to Vision-Based Terrain-Aided Localization
Terrain-aided navigation, which combines radar altitude with a digital terrain map (DTM), was developed before the era of the Global Positioning System to prevent error growth resulting from inertial navigation. Recently, cameras and substantial computational power have become ubiquitous in flying p...
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Institute of Navigation
2025-02-01
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| Series: | Navigation |
| Online Access: | https://navi.ion.org/content/72/1/navi.683 |
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| author | Dan Navon Ehud Rivlin Hector Rotstein |
| author_facet | Dan Navon Ehud Rivlin Hector Rotstein |
| author_sort | Dan Navon |
| collection | DOAJ |
| description | Terrain-aided navigation, which combines radar altitude with a digital terrain map (DTM), was developed before the era of the Global Positioning System to prevent error growth resulting from inertial navigation. Recently, cameras and substantial computational power have become ubiquitous in flying platforms, prompting interest in studying whether the radar altimeter can be replaced by a visual sensor.
This paper presents a novel approach to vision-based terrain-aided localization by revisiting the correspondence and DTM (C-DTM) problem. We demonstrate that we can simplify the C-DTM problem by dividing it into a structure-from-motion (SFM) problem and then anchoring the solution to the terrain. The SFM problem can be solved using existing techniques such as feature detection, matching, and triangulation wrapped with a bundle adjustment algorithm. Anchoring is achieved by matching the point cloud to the terrain using ray-tracing and a variation of the iterative closest point method. One of the advantages of this two-step approach is that an innovative outlier filtering scheme can be included between the two stages to enhance overall robustness.
The resulting algorithm consistently demonstrated high precision and statistical independence in the presence of initial errors across various simulations. The impact of different filtering methods was also studied, showing an improvement of 50% compared with the unfiltered case.
The new algorithm has the potential to improve localization in real-world scenarios, making it a suitable candidate for pairing with an inertial navigation system and a Kalman f ilter to construct a comprehensive navigation system. |
| format | Article |
| id | doaj-art-45bb855947de45e1b7bce8d9de9c7907 |
| institution | OA Journals |
| issn | 2161-4296 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Institute of Navigation |
| record_format | Article |
| series | Navigation |
| spelling | doaj-art-45bb855947de45e1b7bce8d9de9c79072025-08-20T02:32:44ZengInstitute of NavigationNavigation2161-42962025-02-0172110.33012/navi.683navi.683A Robust Approach to Vision-Based Terrain-Aided LocalizationDan NavonEhud RivlinHector RotsteinTerrain-aided navigation, which combines radar altitude with a digital terrain map (DTM), was developed before the era of the Global Positioning System to prevent error growth resulting from inertial navigation. Recently, cameras and substantial computational power have become ubiquitous in flying platforms, prompting interest in studying whether the radar altimeter can be replaced by a visual sensor. This paper presents a novel approach to vision-based terrain-aided localization by revisiting the correspondence and DTM (C-DTM) problem. We demonstrate that we can simplify the C-DTM problem by dividing it into a structure-from-motion (SFM) problem and then anchoring the solution to the terrain. The SFM problem can be solved using existing techniques such as feature detection, matching, and triangulation wrapped with a bundle adjustment algorithm. Anchoring is achieved by matching the point cloud to the terrain using ray-tracing and a variation of the iterative closest point method. One of the advantages of this two-step approach is that an innovative outlier filtering scheme can be included between the two stages to enhance overall robustness. The resulting algorithm consistently demonstrated high precision and statistical independence in the presence of initial errors across various simulations. The impact of different filtering methods was also studied, showing an improvement of 50% compared with the unfiltered case. The new algorithm has the potential to improve localization in real-world scenarios, making it a suitable candidate for pairing with an inertial navigation system and a Kalman f ilter to construct a comprehensive navigation system.https://navi.ion.org/content/72/1/navi.683 |
| spellingShingle | Dan Navon Ehud Rivlin Hector Rotstein A Robust Approach to Vision-Based Terrain-Aided Localization Navigation |
| title | A Robust Approach to Vision-Based Terrain-Aided Localization |
| title_full | A Robust Approach to Vision-Based Terrain-Aided Localization |
| title_fullStr | A Robust Approach to Vision-Based Terrain-Aided Localization |
| title_full_unstemmed | A Robust Approach to Vision-Based Terrain-Aided Localization |
| title_short | A Robust Approach to Vision-Based Terrain-Aided Localization |
| title_sort | robust approach to vision based terrain aided localization |
| url | https://navi.ion.org/content/72/1/navi.683 |
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