Dingnan Zhang

Papers

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About

Dingnan Zhang is a researcher at the forefront of efficient autonomous systems, with a primary focus on embedded computer vision, SLAM (Simultaneous Localization and Mapping), and sensor fusion for robotics and autonomous driving. His most influential work, "Evaluating the Power Efficiency of Visual SLAM on Embedded GPU Systems" (2019, 21 citations), provides a critical benchmark for deploying ORB-SLAM2 on power-constrained platforms, demonstrating how GPU acceleration can balance performance and energy consumption—a key challenge for mobile robots reliant on battery power. More recently, Zhang has advanced the field of autonomous driving with "Optimized Deep Learning for LiDAR and Visual Odometry Fusion" (2023, 5 citations), which tackles the safety-critical problem of accurate pose estimation in complex, dynamic environments. By integrating deep learning with multi-modal sensor data, his work addresses the limitations of traditional visual odometry algorithms, paving the way for more reliable and robust localization. Zhang’s contributions are particularly notable for bridging the gap between theoretical algorithm design and practical embedded system constraints, making his research highly relevant for engineers and researchers developing next-generation autonomous vehicles and mobile robots.