To explore the technical principles of absolute positioning accuracy evaluation indicators, specific code examples are required
Abstract:
Absolute positioning is a very important part of modern navigation systems. In order to evaluate the accuracy of absolute positioning, some evaluation indicators need to be used. This article will introduce some commonly used absolute positioning accuracy evaluation indicators and explain their technical principles in detail. At the same time, some specific code examples will also be given to help readers better understand these evaluation indicators and how to implement them.
1.2 Purpose of this article
The purpose of this article is to introduce some commonly used absolute positioning accuracy evaluation indicators and explain their technical principles in detail. At the same time, in order to help readers better understand these indicators, we will also give some specific code examples. By reading this article, readers can have a deeper understanding of the accuracy evaluation process of absolute positioning.
import numpy as np def rmse(estimated, true): error = estimated - true sqr_error = np.square(error) mean_error = np.mean(sqr_error) return np.sqrt(mean_error)
2.2 MAE (Mean Absolute Error)
MAE is also a commonly used absolute positioning accuracy evaluation index. It is similar to RMSE, except that it uses the absolute value of the error. The calculation formula of MAE is as follows:
import numpy as np def mae(estimated, true): error = estimated - true abs_error = np.abs(error) mean_error = np.mean(abs_error) return mean_error
import numpy as np def rmsd(estimated, true): diff = estimated - true sqr_diff = np.square(diff) mean_diff = np.mean(sqr_diff) return np.sqrt(mean_diff)
3.2 RPE (Relative Pose Error)
RPE is also a commonly used distance measurement index between cubes. It can measure the target position error in the estimation of relative attitude. The calculation formula of RPE is as follows:
import numpy as np def rpe(estimated, true): abs_diff = np.abs(estimated - true) abs_diff_norm = np.linalg.norm(abs_diff, axis=1) mean_error = np.mean(abs_diff_norm) return mean_error
References:
[1] Zhang, H., Pillai, S. U., & Nebot, E. M. (2020). Performance Evaluation Metrics for Mobile Robot Localization. arXiv preprint arXiv:2005.02011.
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