fs-feup/autonomous-systems
Jan 2026 – Jan 2026
1 Month active
Languages Used
C++PythonYAML
Technical Skills
C++ programmingPythonROSalgorithm designcontrol systemsrobotics
Diogo Pinheiro
PROFILE
Diogo Pinheiro
Diogo Pinheiro contributed to the fs-feup/autonomous-systems repository by developing an IMU-based motion tracking integrator that processes sensor data for improved velocity estimation. Using C++ and ROS, he implemented calibration routines and gravity compensation to enhance motion tracking accuracy, directly addressing drift and stability issues in autonomous navigation. Diogo also resolved a startup steering bug by tuning control parameters and refining the lookahead logic within the pathfinding algorithm, resulting in more reliable early-run behavior. His work demonstrated depth in algorithm design and sensor integration, delivering robust solutions that improved both the safety and operational readiness of the autonomous robotics platform.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
405
Activity Months1
Your Network
4 peopleShared Repositories
4
Work History
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly performance summary for fs-feup/autonomous-systems focusing on delivering core sensor fusion capabilities and improving startup reliability. Key features were implemented with careful calibration and gravity compensation to enhance motion tracking accuracy, while a startup steering issue was fixed through targeted control parameter tuning and lookahead logic adjustments. The work improves autonomous navigation safety, reliability, and operational readiness.
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly performance summary for fs-feup/autonomous-systems focusing on delivering core sensor fusion capabilities and improving startup reliability. Key features were implemented with careful calibration and gravity compensation to enhance motion tracking accuracy, while a startup steering issue was fixed through targeted control parameter tuning and lookahead logic adjustments. The work improves autonomous navigation safety, reliability, and operational readiness.
January 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonYAML
Technical Skills
C++ programmingPythonROSalgorithm designcontrol systemsroboticssensor integration
Repositories Contributed To
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