Max Robotics — Global Service Robot Directory + US Certification

About

M. Tordon is a robotics researcher whose work focuses on the foundational challenges of mobile robot autonomy, particularly in mapping, localization, and motion modeling. His most influential contribution is the development of a line segment-based scan matching algorithm for concurrent mapping and localization (SLAM) in unknown environments. This work, which has garnered 13 citations, provides a robust method for aligning laser scans without relying on physical landmarks, enabling a robot to build a consistent map of its surroundings from scratch. Tordon also made significant contributions to robot control and probabilistic modeling. He designed and implemented a PC-based controller for a PUMA 512 robot, replacing its original system with a real-time, direct-control architecture. Furthermore, he introduced a novel, fully probabilistic procedure for extracting the odometry motion model of an autonomous wheelchair, a method generalizable to any mobile robot and crucial for the effectiveness of particle filter-based localization and navigation. His work also extends to active-vision systems for recognizing moving objects. With a career spanning from low-level control to high-level probabilistic reasoning, Tordon’s research provides essential building blocks for autonomous navigation.