EPFLRocketTeam/2024_C_AV_RPI
Mar 2025 – Mar 2025
1 Month active
Languages Used
C++CMakeEigen
Technical Skills
C++CMakeEigen LibraryEmbedded SystemsKalman FilterQuaternion Algebra
AnasHimmi
PROFILE
Anashimmi
Developed a foundational Kalman Filter module for navigation and state estimation in the EPFLRocketTeam/2024_C_AV_RPI repository, focusing on robust real-time orientation, velocity, and position estimation for rocket control. The work centered on implementing an error-state Kalman Filter with a multiplicative quaternion formulation, encapsulated in a core Kalman class featuring initialization, prediction, and update methods. Additional utilities included rotation conversions and static calibration to support sensor bias correction. Using C++, CMake, and the Eigen library, the developer established initial build-system integration and supporting data structures, laying the architectural groundwork for future simulation validation, sensor integration, and hardware-in-the-loop testing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
680
Activity Months1
Your Network
29 peopleShared Repositories
9
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly performance summary for EPFLRocketTeam/2024_C_AV_RPI. Focused on delivering a foundational Kalman Filter module for navigation and state estimation, enabling robust real-time orientation, velocity, and position estimates and sensor bias calibration for rocket control. Implemented an error-state Kalman Filter with multiplicative quaternion formulation and a core Kalman class with initialization, prediction, and update methods, plus rotation conversions and static calibration utilities. Established initial build-system integration and data structures to support navigation workflows. Milestone achieved: first full version of the Kalman module completed (not yet tested or compiled). No major bugs fixed this month; primary effort centered on feature development and architectural groundwork to accelerate flight-ready navigation. Next steps include simulation validation, unit tests, and hardware-in-the-loop verification, along with sensor integration and performance benchmarking.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly performance summary for EPFLRocketTeam/2024_C_AV_RPI. Focused on delivering a foundational Kalman Filter module for navigation and state estimation, enabling robust real-time orientation, velocity, and position estimates and sensor bias calibration for rocket control. Implemented an error-state Kalman Filter with multiplicative quaternion formulation and a core Kalman class with initialization, prediction, and update methods, plus rotation conversions and static calibration utilities. Established initial build-system integration and data structures to support navigation workflows. Milestone achieved: first full version of the Kalman module completed (not yet tested or compiled). No major bugs fixed this month; primary effort centered on feature development and architectural groundwork to accelerate flight-ready navigation. Next steps include simulation validation, unit tests, and hardware-in-the-loop verification, along with sensor integration and performance benchmarking.
March 2025
Activity
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Quality Metrics
Correctness75.0%
Maintainability85.0%
Architecture85.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeEigen
Technical Skills
C++CMakeEigen LibraryEmbedded SystemsKalman FilterQuaternion AlgebraRoboticsSensor Fusion
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