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
Florencia Battochia developed a new LikelihoodFieldProb sensor model for the Ekumen-OS/beluga repository, focusing on enhancing perception reliability in unmapped environments. She implemented logarithmic probability summation within the model to address numerical underflow, and integrated a Cluster-Based Estimation algorithm to improve robustness against unmapped objects. Her work involved C++ development, algorithm implementation, and sensor modeling, with comprehensive unit tests to ensure correctness and maintainability. By addressing the challenges of probabilistic robotics and sensor data interpretation, Florencia delivered a feature that enables safer autonomous operation, demonstrating depth in both technical approach and attention to code quality within a complex robotics context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
700
Activity Months1
Your Network
11 people
Work 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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