Pep Lluis N.

Deliver end-to-end robotics solutions, from low-level perception and sensor fusion to high-level autonomy and system integration, tailored to mobile and subsea platforms. Develop and integrate AI/ML models into robotic applications for tasks such as object detection, semantic segmentation of 3D structures, anomaly detection, and mission decision-making. Lead and contribute to the development of production-grade robotics software, including: SubSLAM (a real-time, live 3D stereo-vision system for subsea inspection) and the world-first subsea Autonomous Inspection Algorithm for offshore infrastructure using vision, mapping, and ML components. Build custom pipelines for real-time point cloud processing, dense 3D reconstruction, and SLAM, with robust performance in visually degraded or GPS-denied environments. Apply AI/ML techniques to improve robustness in challenging conditions, e.g.,: Deep-learning-based feature detection and matching. Point cloud segmentation using architectures like PointNet++. Domain adaptation strategies bridging synthetic and real-world perception data. Co-hold patents in subsea vision and detection systems, with experience deploying novel algorithms in safety-critical, real-world conditions. Collaborate across disciplines, guiding cross-functional teams and interfacing between software, hardware, and field operations.

Leading the development of world-class AI-driven subsea autonomy. Invented and delivered the world-first autonomous inspection technology for an offshore jacket structure. Driving the end-to-end architecture and algorithmic design, from perception and sensor fusion to decision-making and mission autonomy. Leading a multidisciplinary tech team, setting technical direction, and ensuring delivery of high-impact, production-grade solutions.

Designing online 3D underwater mapping algorithms, based on an accurate Visual-SLAM positioning system and semi-dense 3D reconstructions. Developing cutting-edge solutions for reliable and impressive 3D underwater reconstructions to be executed online during the robot mission.

Working on underwater robotics and computer vision focused on robot localization and navigation, 3D reconstruction and 3D model-based object recognition. PUBLICATIONS: P. L. Negre, F. Bonin-Font, G. Oliver. Global Image Signature for Visual Loop-Closure Detection. In Autonomous Robots, Published Online, Springer US, vol. 40, pp. 1403-1417, December, 2016. P. L. Negre, F. Bonin-Font, G. Oliver. Cluster-Based Loop Closing Detection for Underwater SLAM in Feature-Poor Regions. In IEEE International Conference on Robotics and Automation (ICRA), Stockholm (Sweeden), 2016. BEST STUDENT PAPER AWARD FINALIST. P. L. Negre, F. Bonin-Font, M. Massot, G. Oliver. Stereo-Vision Graph-SLAM for Robust Navigation of the AUV SPARUS II. In IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles (NGCUV), Girona, 2015. P. L. Negre, F. Bonin-Font, G. Oliver. Stereo Graph-SLAM for Autonomous Underwater Vehicles. In Intelligent Autonomous Systems 13 (Proceedings of the 13th International Conference on Intelligent Autonomous Systems IAS13), Padova/Venice, pp. 351-360, 2014.

Create value, manage the group and keep developing code in HTML5, Javascript, CSS, PHP & NodeJS.

Developing drivers for IAS/Wonderware, cameras, relies and much more. Implementing power monitoring systems to improve the building efficiency with powerful web front-ends and databases. Enhanced the experience in C#, Java, HTML, PHP and JS.

Controller tuning and development of new control strategies. Specializing in system identification and fault-tolerant control.

Matlab programmer. Developing algorithms for signal processing.

Repair services after-sales.