Keshaw Dewangan

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About

Keshaw Dewangan is a robotics researcher focused on advancing autonomous systems through multi-sensor integration and 3D perception. His key research areas include mobile robotics, computer vision, and environment reconstruction, with a particular emphasis on developing platforms capable of substituting humans in hazardous or complex indoor missions. Dewangan’s major contribution lies in designing a next-generation mobile robot that fuses multiple sensing modalities—including monocular vision—to achieve robust 3D perception and real-time environment mapping. His 2015 paper on this platform, which has garnered 12 citations, demonstrates how multi-sensor fusion enables robots to navigate and reconstruct indoor spaces with high fidelity, a critical capability for search-and-rescue, surveillance, and industrial inspection. In related work (2016, 5 citations), he further refined monocular vision-based 3D reconstruction, showing that cost-effective single-camera systems can achieve accurate spatial awareness when paired with intelligent algorithms. Dewangan’s research bridges the gap between theoretical computer vision and practical robotic deployment, offering scalable solutions for autonomous navigation. His work is particularly notable for addressing real-world constraints like sensor noise and computational efficiency, making it directly applicable to next-generation service and emergency-response robots.