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About

Manolis Lourakis is a computer vision and robotics researcher whose work has advanced autonomous navigation and geometric estimation. His research primarily focuses on obstacle detection, assistive navigation, and efficient 3D point-set alignment—all critical for enabling mobile robots to perceive and move safely through their environments. Lourakis’s most influential contribution is his pioneering work on vision-based obstacle detection, notably his 1997 paper “Visual detection of obstacles assuming a locally planar ground,” which has accumulated 50 citations and established a foundational method for ground-plane obstacle perception. He also developed innovative approaches for robotic wheelchair navigation, combining vision and range sensors to provide advanced navigational support without requiring environmental modifications. In the domain of geometric computation, Lourakis has made significant strides in solving the absolute orientation problem—determining the similarity transformation between 3D point sets. His 2018 paper “Efficient Absolute Orientation Revisited” and related works introduced faster, more robust algorithms that avoid expensive matrix factorizations, with implementations even accelerated on FPGA hardware. With over 120 total citations across his most-cited works, Lourakis’s research continues to influence practical robotics applications, from assistive wheelchairs to real-time vision systems.