Daniel Welfer
Papers
3
Total Citations
113
H-Index
3
About
Daniel Welfer is a leading researcher in mobile robotics and computer vision, with a focus on integrating deep learning and sensor fusion for autonomous navigation. His most cited work, "Mobile Robot Navigation Using an Object Recognition Software with RGBD Images and the YOLO Algorithm" (2019, 90 citations), introduces a real-time vision system that leverages the YOLO algorithm and Microsoft Kinect RGBD sensors to detect static obstacles, enabling mobile robots to navigate dynamic environments. By deploying this system on an Nvidia Jetson TX2 GPU, Welfer achieved efficient, on-board processing, a key contribution to embedded robotics. He further advanced human-robot interaction in his 2019 paper on gesture recognition (13 citations), where he combined Convolutional Neural Networks with FastDTW to interpret user movements from Kinect skeletal data, controlling robot motion. Welfer’s work bridges object detection and activity recognition, demonstrating how low-cost sensors and deep learning can create responsive, intelligent robotic systems. His high-impact research has been widely cited for its practical applications in autonomous navigation and human-robot collaboration, establishing him as a notable figure in the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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