Stanford-AUV/RoboSub
Feb 2026 – Feb 2026
1 Month active
Languages Used
BashC++PythonShellYAML
Technical Skills
Build automationC++DockerKalman filteringLaunch file configurationPython
Hemal Arora
PROFILE
Hemal Arora
Worked on the Stanford-AUV/RoboSub repository to enhance the sensor fusion framework by implementing a refined Generic Sensor module with a stable interface and improved plotting capabilities. Leveraged Python, C++, and YAML configuration to move covariance data into sensors.yaml, updating both the EKF and DVL modules for more robust state estimation and sensor integration. Addressed IMU bugs and streamlined calibration code to improve data integrity, while strengthening build automation through build.sh fixes and publisher reliability improvements. Updated hardware launch files to support real-time plotting, enabling better on-vehicle diagnostics and data visualization, and standardized hardware code naming for maintainability.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
26Total
Bugs
3
Commits
26
Features
7
Lines of code
116,805
Activity Months1
Your Network
7 peopleWork History
February 2026
26 Commits • 7 Features
Feb 1, 2026February 2026 – RoboSub: Strengthened the sensor fusion backbone, improved reliability, and enhanced diagnostics. Delivered a refined Generic Sensor module with a stable interface, standardized hardware code naming, and plotting readiness. Implemented covariance-driven fusion by moving covariances into sensors.yaml and updating EKF/DVL to consume them. Updated DVL integration to align with sensors.yaml covs and refreshed EKF configuration. Fixed IMU bugs and cleaned up calibration code and pubs, improving data integrity. Hardened the build and deployment workflow with build.sh and publishers fixes, plus hardware launch plotting support for better on-vehicle diagnostics.
26 Commits • 7 Features
Feb 1, 2026February 2026 – RoboSub: Strengthened the sensor fusion backbone, improved reliability, and enhanced diagnostics. Delivered a refined Generic Sensor module with a stable interface, standardized hardware code naming, and plotting readiness. Implemented covariance-driven fusion by moving covariances into sensors.yaml and updating EKF/DVL to consume them. Updated DVL integration to align with sensors.yaml covs and refreshed EKF configuration. Fixed IMU bugs and cleaned up calibration code and pubs, improving data integrity. Hardened the build and deployment workflow with build.sh and publishers fixes, plus hardware launch plotting support for better on-vehicle diagnostics.
February 2026
Activity
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Quality Metrics
Correctness91.6%
Maintainability86.2%
Architecture87.0%
Performance86.2%
AI Usage22.4%
Skills & Technologies
Programming Languages
BashC++PythonShellYAML
Technical Skills
Build automationC++DockerKalman filteringLaunch file configurationPythonPython programmingPython scriptingROSROS (Robot Operating System)Shell scriptingYAML configurationconfiguration managementdata processingdata visualization
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