Eeshan Gunesh Dhekane

Papers

Total Citations

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About

Eeshan Gunesh Dhekane is a researcher at the intersection of computer vision, deep learning, and mobile robotics, with a focus on enabling efficient, real-time spatial intelligence for autonomous systems. His most-cited work, "Convolutional Neural Network Based Sensors for Mobile Robot Relocalization" (2018, 13 citations), tackles a critical bottleneck in robotics: the computational expense of deep CNNs for camera pose estimation. Dhekane’s contribution lies in demonstrating that lightweight, compact CNN architectures can achieve accurate relocalization without the heavy GPU requirements typical of deeper networks, making them viable for resource-constrained mobile robots. This work bridges the gap between state-of-the-art deep learning and practical deployment, highlighting his ability to address real-world constraints like latency and power consumption. While his citation count reflects a focused, early-career impact, his research underscores a commitment to efficient, scalable AI for robotics. Dhekane’s approach is particularly notable for its emphasis on sensor-level integration, paving the way for more responsive and autonomous navigation systems in dynamic environments.