Avionics-Propulsion-Landers-GT/MonopropUAV
Mar 2026 – Mar 2026
1 Month active
Languages Used
BashPythonRust
Technical Skills
3D animationKalman filteringPython programmingRust programmingalgorithm developmentdata analysis
aneesh-iyer29
PROFILE
Aneesh-iyer29
Aneesh Iyer developed a robust 9-axis IMU Extended Kalman Filter for the Avionics-Propulsion-Landers-GT/MonopropUAV repository, delivering end-to-end state estimation for UAV attitude, altitude, and position. He implemented the EKF in Rust, focusing on numerical stability by refactoring update logic and covariance handling, and addressed sensor noise through gravity and magnetic reference refinements. Aneesh integrated ground-truth data utilities and automated Python-based visualization, enabling streamlined validation and reducing manual effort. His work established a scalable testing framework and matrix-input support, laying the groundwork for future multi-sensor fusion and contributing to safer UAV navigation and more reliable flight control.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
1
Lines of code
22,194
Activity Months1
Your Network
22 peopleShared Repositories
22
Work History
March 2026
8 Commits • 1 Features
Mar 1, 2026Month: 2026-03 | Repository: Avionics-Propulsion-Landers-GT/MonopropUAV Summary: - Delivered a robust 9-axis IMU EKF in Rust, establishing an end-to-end state estimation pipeline for attitude, altitude, and position with an integrated testing framework, ground-truth utilities, and Python-based visualization. Refactored EKF update logic and covariance handling to improve numerical stability and robustness to sensor noise; implemented gravity/magnetic reference refinements; added matrix-input support and an automated test/visualization script to streamline validation. - Fixed critical issues including gravity reference normalization and initial covariance handling, resulting in more reliable attitude estimation and faster validation cycles. - Created a scalable validation loop with automated scripts, enabling end-to-end testing against ground-truth data and visualization, reducing manual validation effort. - Prepared the foundation for future multi-sensor fusion and extendable state estimation, enabling safer UAV navigation and more accurate flight control decisions. Overall impact: - Business value: enhanced flight safety and mission reliability through accurate state estimation and faster validation; reduced risk from sensor noise and miscalibrations; accelerated development cycles for future sensor suites. - Technical achievements: Rust-based EKF, testing/visualization tooling, ground-truth integration, gravity/magnetic reference calibration, matrix-input support, and automated validation pipelines. Technologies/skills demonstrated: - Rust, EKF/state estimation, numerical stability improvements, sensor fusion concepts; Python-based visualization; automated testing scripts; ground-truth data integration; refactoring for maintainability and performance.
8 Commits • 1 Features
Mar 1, 2026Month: 2026-03 | Repository: Avionics-Propulsion-Landers-GT/MonopropUAV Summary: - Delivered a robust 9-axis IMU EKF in Rust, establishing an end-to-end state estimation pipeline for attitude, altitude, and position with an integrated testing framework, ground-truth utilities, and Python-based visualization. Refactored EKF update logic and covariance handling to improve numerical stability and robustness to sensor noise; implemented gravity/magnetic reference refinements; added matrix-input support and an automated test/visualization script to streamline validation. - Fixed critical issues including gravity reference normalization and initial covariance handling, resulting in more reliable attitude estimation and faster validation cycles. - Created a scalable validation loop with automated scripts, enabling end-to-end testing against ground-truth data and visualization, reducing manual validation effort. - Prepared the foundation for future multi-sensor fusion and extendable state estimation, enabling safer UAV navigation and more accurate flight control decisions. Overall impact: - Business value: enhanced flight safety and mission reliability through accurate state estimation and faster validation; reduced risk from sensor noise and miscalibrations; accelerated development cycles for future sensor suites. - Technical achievements: Rust-based EKF, testing/visualization tooling, ground-truth integration, gravity/magnetic reference calibration, matrix-input support, and automated validation pipelines. Technologies/skills demonstrated: - Rust, EKF/state estimation, numerical stability improvements, sensor fusion concepts; Python-based visualization; automated testing scripts; ground-truth data integration; refactoring for maintainability and performance.
March 2026
Activity
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Quality Metrics
Correctness85.0%
Maintainability82.6%
Architecture82.6%
Performance82.6%
AI Usage25.0%
Skills & Technologies
Programming Languages
BashPythonRust
Technical Skills
3D animationKalman filteringPython programmingRust programmingalgorithm developmentdata analysisdata processingdata visualizationfilter designmathematicsroboticsscriptingsensor fusiontesting
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