Ahmad Karambakhsh

Shanghai Jiao Tong University, Qazvin Islamic Azad University, Islamic Azad University, Tehran

Papers

Total Citations

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About

Ahmad Karambakhsh is a researcher whose work bridges the critical domains of 3D computer vision and autonomous mobile robotics. His most impactful contribution, "SparseVoxNet: 3-D Object Recognition With Sparsely Aggregation of 3-D Dense Blocks" (2022), has garnered 76 citations, demonstrating its significance in the field. This work addresses the challenge of efficient 3D object recognition using convolutional neural networks, introducing a novel architecture that leverages sparsely aggregated 3D dense blocks. This approach is vital for applications in robotics and augmented reality, where processing complex 3D models quickly and accurately is paramount. Earlier in his career, Karambakhsh focused on the foundational challenges of mobile robot navigation. His 2009 paper on "Object based navigation of mobile robot with obstacle avoidance using fuzzy controller" (9 citations) and his 2011 work on "Robot navigation algorithm to wall following using fuzzy Kalman filter" (4 citations) tackled the enduring problem of autonomous navigation in unknown and crowded environments. By integrating fuzzy logic with Kalman filtering, he contributed to making robots more adept at path planning and obstacle avoidance, which are essential for reliable localization and mapping. Karambakhsh’s research trajectory shows a clear evolution from solving practical navigation problems to advancing the deep learning techniques that power modern robotic perception.