Max Robotics — Global Service Robot Directory + US Certification

About

Marcel Kyas is a leading researcher in indoor localization and positioning systems, with a focus on developing robust evaluation methodologies and filtering techniques for wireless sensor networks. His major contributions include the creation of a low-cost, mobile robot-based reference system for indoor localization testbeds, which provides precise ground truth data for evaluating a wide range of localization algorithms—a work that has garnered 46 citations. Kyas has also advanced numerical methods for recursive Bayesian estimation, notably through the Recursively Bounded Grid-Based Filter (RBGF) and the Geometric Bayesian filter (GeoF), which address challenges like limited processing power and inaccurate ranging in mixed line-of-sight and non-line-of-sight conditions. His virtual testbed for indoor localization enables researchers to easily test algorithms on real-world range-based data, enhancing reproducibility and comparability in the field. Beyond localization, Kyas has explored fiducial systems for mobile robot pose estimation, evaluating orientation ambiguity in AprilTag and WhyCode, with implications for autonomous drone landing. With over 90 citations across his most-cited works, Kyas’s research is instrumental in bridging the gap between simulation and real-world deployment, providing essential tools and insights for the next generation of indoor positioning technologies.