Aniket Gupta

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About

Aniket Gupta is a robotics researcher whose work tackles two critical challenges in autonomous systems: robust perception and real-time efficiency. His primary research areas include Simultaneous Localization and Mapping (SLAM) and optical flow estimation, with a focus on bridging the gap between algorithmic accuracy and practical deployment. Gupta’s major contribution, the "Challenges of Indoor SLAM" dataset (2023, 9 citations), provides a multi-modal, multi-floor benchmark that exposes the fragility of state-of-the-art SLAM algorithms in real-world environments—a vital resource for the field. More recently, his work on "NeuFlow-V2" (2025, 5 citations) pushes the boundaries of high-efficiency optical flow, achieving a rare balance between precision and computational speed for resource-constrained robotic platforms. By identifying key failure modes in SLAM and advancing efficient visual perception, Gupta’s research directly supports the next generation of reliable, real-time autonomous navigation. His contributions are particularly notable for their practical impact, offering both diagnostic tools and algorithmic solutions that move robotics closer to seamless indoor deployment.