SUMMARY
This discussion focuses on comparing Euler angles and quaternions derived from user data against corresponding sets obtained from STK (Systems Tool Kit). The user seeks effective metrics for measuring discrepancies between these sets, specifically inquiring about the suitability of Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD) as indicators. The consensus indicates that both RMSE and MAD are valid metrics for quantifying the error in this context.
PREREQUISITES
- Understanding of Euler angles and quaternions in 3D space representation
- Familiarity with STK (Systems Tool Kit) software
- Knowledge of statistical error metrics, specifically RMSE and MAD
- Basic proficiency in data comparison techniques
NEXT STEPS
- Research the implementation of RMSE and MAD in Python using libraries like NumPy
- Explore advanced error metrics for 3D transformations, such as Quaternion Distance
- Learn about the conversion processes between Euler angles and quaternions
- Investigate the capabilities of STK for data validation and error analysis
USEFUL FOR
Data scientists, aerospace engineers, and software developers working with 3D spatial data and seeking to validate orientation data against established benchmarks.