Pradyumn Chaturvedi

Visvesvaraya National Institute of Technology

Papers

Total Citations

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About

Pradyumn Chaturvedi is a researcher whose work sits at the intersection of autonomous robotics and applied deep learning. His primary research areas include computer vision for mobile robotics, stair detection and traversal, and statistical image filtering for real-world navigation. Chaturvedi’s most notable contribution is his deep learning-based stair detection system, which enables autonomous stair climbing—a critical capability for robots operating in complex, unstructured environments like disaster zones or multi-story buildings. His 2019 paper on this topic, which has garnered 38 citations, demonstrates how convolutional neural networks can be paired with statistical image filtering to reliably identify staircases, overcoming challenges posed by varying lighting, textures, and perspectives. This work has direct implications for urban search and rescue, surveillance, and military robotics, where traversing stairs is often a bottleneck for autonomous systems. Chaturvedi’s research stands out for its practical focus on bridging the gap between lab-based vision algorithms and field-ready robotic platforms. By tackling a specific, high-impact problem with a modern deep learning toolkit, he has provided a foundation for safer and more capable mobile robots in hazardous environments.