Papers
29
Total Citations
927
H-Index
12
About
Nived Chebrolu is a robotics and computer vision researcher whose work sits at the dynamic intersection of agricultural robotics, precision farming, and autonomous navigation. His most influential contribution — a large-scale agricultural robot dataset for plant classification, localization, and mapping on sugar beet fields (2017, 327 citations) — became a foundational resource for the field, addressing the critical scarcity of high-quality agricultural training data. Building on this, Chebrolu has driven significant advances in aerial-ground robotic systems for precision farming, crop-weed classification, and plant-specific treatment strategies, collectively garnering hundreds of citations and helping redefine sustainable, chemical-reduced agriculture. Beyond the farm, his research extends into robust autonomous navigation, including visual teach-and-repeat systems, fast traversability estimation for wild environments, and scalable LiDAR-visual reconstruction using neural radiance fields. His PhenoBench dataset (2024) further demonstrates his commitment to rigorous benchmarking in agricultural vision. Throughout his career, Chebrolu has consistently bridged fundamental computer vision research with real-world robotic deployments, producing tools and datasets that empower the broader research community. With over 800 cumulative citations, his work stands as a compelling case for how intelligent robotics can meaningfully address global food security and environmental sustainability challenges.
Research Focus
Key Achievements
Top Papers
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- 4Fast Traversability Estimation for Wild Visual Navigation62 citations · 2023
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- 7Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants35 citations · 2020
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