Ekumen-OS/beluga
Apr 2025 – Apr 2025
1 Month active
Languages Used
C++CMake
Technical Skills
Algorithm ImplementationC++ DevelopmentProbabilistic RoboticsRoboticsSensor ModelingUnit Testing
fbattocchia
PROFILE
Fbattocchia
Developed and integrated a LikelihoodFieldProb sensor model for the Ekumen-OS/beluga repository, focusing on enhancing perception reliability in unmapped environments. The implementation used logarithmic probability summation in C++ to address numerical underflow, and incorporated Cluster-Based Estimation to improve robustness against unmapped objects. Unit tests were added to ensure correctness and maintainability of the new model. This work demonstrated skills in algorithm implementation, probabilistic robotics, and sensor modeling, while leveraging C++ and CMake for development and build management. The resulting feature improved the system’s ability to perceive and operate safely in environments with incomplete or missing map data.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
700
Activity Months1
Your Network
12 peopleWork History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 - Ekumen-OS/beluga: Implemented LikelihoodFieldProb sensor model with log-domain probability summation to prevent underflow and integrated Cluster-Based Estimation to improve robustness against unmapped objects. Added unit tests for the new model. Commit 876fc117c7de47cfc7692b8c0f61e09257418189 ('Add likelihood prob sensor model and enable Beluga's flavour of clustering', #476). Business value: more reliable perception in unmapped environments, enabling safer autonomous operation. Technologies demonstrated: sensor-model engineering, logarithmic probability math, clustering algorithms, unit testing, and code quality.
1 Commits • 1 Features
Apr 1, 2025April 2025 - Ekumen-OS/beluga: Implemented LikelihoodFieldProb sensor model with log-domain probability summation to prevent underflow and integrated Cluster-Based Estimation to improve robustness against unmapped objects. Added unit tests for the new model. Commit 876fc117c7de47cfc7692b8c0f61e09257418189 ('Add likelihood prob sensor model and enable Beluga's flavour of clustering', #476). Business value: more reliable perception in unmapped environments, enabling safer autonomous operation. Technologies demonstrated: sensor-model engineering, logarithmic probability math, clustering algorithms, unit testing, and code quality.
April 2025
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMake
Technical Skills
Algorithm ImplementationC++ DevelopmentProbabilistic RoboticsRoboticsSensor ModelingUnit Testing
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