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About

Archana Khurana is a researcher whose work lies at the intersection of robotics, computer vision, and 3D sensing, with a particular focus on enabling mobile robots to perceive and navigate their environments with greater accuracy. Her major contributions center on the calibration and optimization of sensor systems, specifically the fusion of laser range finders (LRFs) and cameras. Her systematic review on extrinsic calibration methods for these sensors (2021, 18 citations) has become a key reference for researchers tackling the fundamental challenge of aligning disparate sensor data. Khurana’s work on signal averaging for noise reduction in mobile robot 3D measurement (2017, 19 citations) directly addresses a critical practical hurdle, improving the reliability of real-world robotic perception. She has also advanced the field of 3D reconstruction, developing an optimized point cloud reconstruction method using gradient descent (2020, 10 citations) and an improved calibration technique for tilting 2D LRFs (2020, 10 citations). With a cumulative citation count exceeding 50, Khurana’s research provides foundational techniques that are essential for applications ranging from autonomous navigation to industrial automation, making her a notable contributor to the practical advancement of robotic vision systems.