Bernd Poppinga

Papers

Total Citations

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About

Bernd Poppinga is a researcher whose work sits at the intersection of robotics and computer vision, with a particular focus on enabling real-time perception for mobile platforms. His most cited contribution, "JET-Net: Real-Time Object Detection for Mobile Robots" (2019), has garnered 24 citations, reflecting its practical significance in the field. This work addresses a critical challenge: deploying accurate object detection on resource-constrained mobile robots without sacrificing speed. Poppinga’s approach likely involves efficient neural network architectures or optimization techniques that balance computational cost and detection performance, making it a valuable reference for researchers working on autonomous navigation, drone systems, or embedded AI. Beyond this flagship paper, his broader research explores how robots can interpret their environment in real time, contributing to the growing body of work on lightweight deep learning models for edge devices. While his citation count is modest, the targeted impact of JET-Net underscores his ability to solve concrete engineering problems, offering a practical toolkit for students and engineers building next-generation mobile robots.