Complete MATLAB Algorithm for Pose Estimation

In this technical documentation, we present a complete MATLAB algorithm designed for pose estimation. The implementation includes detailed numerical methods and coordinate transformation techniques that calculate object orientation and position in 3D space. The algorithm incorporates quaternion-based rotations and sensor fusion approaches, utilizing MATLAB's robust matrix operations and optimization toolbox for efficient computation. We provide comprehensive explanations of each algorithmic component, including coordinate system transformations, rotation matrix calculations, and Kalman filter implementation for sensor data fusion. The code structure demonstrates proper handling of Euler angles, direction cosine matrices, and error correction mechanisms through MATLAB's built-in functions like quat2dcm and rotateframe. Sample implementations show how to process inertial measurement unit (IMU) data, perform coordinate transformations, and visualize results using MATLAB's plotting capabilities. The algorithm demonstrates practical applications in robotics, aerospace navigation, and augmented reality systems, showing significant improvements in accuracy and computational efficiency compared to traditional methods. Note: While this algorithm may be shared for educational and research purposes, publication as an article or commercial product is strictly prohibited due to copyright restrictions. Proper attribution is required when referencing this work.