Kernkonzepte
The author presents a novel PnP algorithm tailored for two-dimensional motion, emphasizing accuracy and performance improvements over traditional 3D algorithms by reducing search space dimensionality and minimizing ambiguous pose estimates.
Zusammenfassung
The content delves into the development of a PnP algorithm specifically designed for cameras constrained to two-dimensional motion, such as those on wheeled robotics platforms. The proposed 2DPnP algorithm leverages geometric and algebraic methods to find an approximate solution iteratively, enhancing accuracy and robustness while reducing computational complexity. By addressing challenges like coplanar configurations and ambiguity in pose estimation, the algorithm showcases promising results in terms of accuracy, performance, and noise resilience. The study also explores alternative initialization strategies and testing methodologies to validate the effectiveness of the 2DPnP algorithm against existing 3D pose estimation techniques.
Statistiken
Leveraging this assumption allows accuracy and performance improvements over 3D PnP algorithms due to the reduction in search space dimensionality.
Our algorithm finds an approximate solution by solving a polynomial system and refines its prediction iteratively to minimize the reprojection error.
The outputs of existing algorithms can be fused with other sensor information as in [9].
This is exactly solvable in the case n = 3, giving rise to the class of P3P algorithms as in [1].
We tested the average translational and rotational errors of each algorithm for numbers of points from 10 to 200.
For these tests, we used 50 points, and each image point was perturbed by Gaussian noise with a standard deviation from 1 to 10 pixels.
We implemented the 2DPnP algorithm in MATLAB and tested it alongside existing PnP algorithms calculating 3D pose.
The raw data, plots, and MATLAB files are available at https://github.com/25wangj/2DPnPToolbox.
Zitate
"Our algorithm finds an approximate solution by solving a polynomial system and refines its prediction iteratively to minimize the reprojection error."
"The proposed initialization strategy was chosen for its ability to consider all correspondences while remaining performant."
"We believe that this algorithm can be fruitfully applied to the vision-based localization of wheeled mobile robots."