| Abstract |
In the area of 3D computer vision, the ability to estimate pose between two cameras under high noise levels while maintaining small reprojection errors reflects the robustness of such pose estimation algorithms. Moreover, maintaining positive depth constraint is another challenging task. Unfortunately, current pose estimation algorithms are often sensitive to noise/outliers and do not always guarantee positive depths. As a standalone task, these algorithms perform a positive sign check and simply discard the points with negative depths after the algorithms are executed. These algorithms do not integrate positive depth constraints into the algorithms themselves. Instead, they do it afterwards. Here, from a comprehensive mathematical derivation, we propose a novel pose estimation algorithm that integrates positive depth constraint into the algorithm itself by estimating the depths directly. The algorithm was competitive in producing small reprojection errors when compared to the state-of-the-art algorithms under both synthetic and real-world tests, while most importantly guaranteeing positive depths. |
| Authors |
Chun Li   , J.E. McInroy
|
| Journal Info |
Nature Portfolio | Scientific Reports , vol: 13
, iss: 1
|
| Publication Date |
12/13/2023 |
| ISSN |
2045-2322 |
Type |
article |
| Open Access |
gold
|
| DOI |
https://doi.org/10.1038/s41598-023-49553-9 |
| Keywords |
Depth Estimation (Score: 0.596619) , Monocular Depth Estimation (Score: 0.553153) , Edge Detection (Score: 0.539556) , Ellipse Detection (Score: 0.530663) , 3D Mapping (Score: 0.523869)
|
