Harikrishnan Madhusudanan

Papers

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About

Harikrishnan Madhusudanan is a researcher advancing the frontier of automated 3D measurement and inspection for manufacturing. His primary research areas include robot-scanner calibration, 3D data stitching, and point cloud-based defect diagnosis. Madhusudanan’s most significant contribution is a novel "eye-in-hand" calibration method for 3D scanner-robot systems, which dramatically reduces data stitching errors during long-term, continuous measurement of large objects. His foundational 2020 paper on this technique has garnered 41 citations, underscoring its impact on the field. He further refined this approach in a subsequent 2020 work, focusing on full automation to enhance usability and precision. Most recently, in 2025, Madhusudanan tackled a critical bottleneck in quality control with an automatic point cloud clustering method for surface defect diagnosis, eliminating the need for manual parameter selection. This work promises to make 3D inspection more accessible and reliable. Through his research, Madhusudanan is enabling more accurate, efficient, and automated measurement systems, directly supporting the advancement of smart manufacturing and industrial metrology.