

December 2025 monthly summary: Delivered a robust autonomous drone perception and exploration stack with multi-sensor fusion, advanced front-view UI, and enhanced object detection. Implemented the Drone Perception Core and Autonomous Exploration Engine, delivering target detection, depth estimation, obstacle awareness, and sensor fusion to enable autonomous exploration with real-time mapping and state estimation. Introduced Front View UI with a dual-window interface to separate flight control from system status, plus manual control via the front window, improving operator experience and safety. Expanded object detection to red/blue/black objects and added performance monitoring and logging, with comprehensive visualization and data persistence. Implemented centralized configuration management (config.py) for rapid parameter tuning, memory-conscious data recording, and performance optimizations (reduced image copies, efficient queues, structured logging, and garbage collection). Fixed critical bugs including visual safety scoring calculation, fusion behavior when no LiDAR matches, and UI rendering anomalies to ensure reliable operation. These changes increased mission reliability, safety, and situational awareness while improving development velocity and maintainability.
December 2025 monthly summary: Delivered a robust autonomous drone perception and exploration stack with multi-sensor fusion, advanced front-view UI, and enhanced object detection. Implemented the Drone Perception Core and Autonomous Exploration Engine, delivering target detection, depth estimation, obstacle awareness, and sensor fusion to enable autonomous exploration with real-time mapping and state estimation. Introduced Front View UI with a dual-window interface to separate flight control from system status, plus manual control via the front window, improving operator experience and safety. Expanded object detection to red/blue/black objects and added performance monitoring and logging, with comprehensive visualization and data persistence. Implemented centralized configuration management (config.py) for rapid parameter tuning, memory-conscious data recording, and performance optimizations (reduced image copies, efficient queues, structured logging, and garbage collection). Fixed critical bugs including visual safety scoring calculation, fusion behavior when no LiDAR matches, and UI rendering anomalies to ensure reliable operation. These changes increased mission reliability, safety, and situational awareness while improving development velocity and maintainability.
November 2025 (OpenHUTB/nn): Delivered the Drone Perception Error Analysis and Evaluation Tools to empower diagnostics of the drone perception model. The feature adds visualization of misclassified samples and generation of classification reports to reveal model weaknesses, alongside minor code-structure improvements and updated documentation. Initial implementation and integration are evidenced by commit 05de970b46bdd09f71fbeefe4545f46e6a5fc967, which documents the error-analysis addition and related cleanup. Overall, this work enables data-driven debugging, accelerates model iteration, and improves maintainability of the drone perception module.
November 2025 (OpenHUTB/nn): Delivered the Drone Perception Error Analysis and Evaluation Tools to empower diagnostics of the drone perception model. The feature adds visualization of misclassified samples and generation of classification reports to reveal model weaknesses, alongside minor code-structure improvements and updated documentation. Initial implementation and integration are evidenced by commit 05de970b46bdd09f71fbeefe4545f46e6a5fc967, which documents the error-analysis addition and related cleanup. Overall, this work enables data-driven debugging, accelerates model iteration, and improves maintainability of the drone perception module.
Concise monthly summary for OpenHUTB/nn focused on delivering clear topic governance, documentation hygiene, and stable scope for Oct 2025. Key outcomes include delivery of an initial Topic Confirmation Setup for Unmanned Vehicle Projects (with topic confirmation and READMEs reflecting selected topics: Unmanned_vehicle_control and drone), followed by a revert to restore prior project scope and avoid feature drift. The month emphasized documentation traceability and change governance to preserve system stability while preparing for the next iteration.
Concise monthly summary for OpenHUTB/nn focused on delivering clear topic governance, documentation hygiene, and stable scope for Oct 2025. Key outcomes include delivery of an initial Topic Confirmation Setup for Unmanned Vehicle Projects (with topic confirmation and READMEs reflecting selected topics: Unmanned_vehicle_control and drone), followed by a revert to restore prior project scope and avoid feature drift. The month emphasized documentation traceability and change governance to preserve system stability while preparing for the next iteration.
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