Rishikesh Madan

Papers

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About

Rishikesh Madan’s research lies at the intersection of computer vision and autonomous robotics, with a focus on real-time object recognition and visual navigation. His work addresses the challenge of enabling robots to perceive and understand their environments efficiently. In his most cited paper, “Design framework for general purpose object recognition on a robotic platform” (2017, 10 citations), Madan proposed a framework that leverages convolutional neural networks for accurate, real-time object detection, advancing the practical deployment of vision systems on resource-constrained robotic platforms. This contribution is particularly notable for bridging the gap between complex deep learning models and real-world robotic applications. Expanding into spatial intelligence, his 2021 study “An experimental comparison of visual SLAM systems” (5 citations) systematically evaluated monocular vSLAM algorithms, providing critical insights into their performance for autonomous navigation. By benchmarking these methods, Madan helped clarify trade-offs between accuracy, speed, and robustness—a valuable guide for researchers and engineers building self-localizing robots. Though early in his career, his work demonstrates a clear commitment to making computer vision both powerful and practical for autonomous systems, laying groundwork for future advances in embodied AI.