Max Robotics — Global Service Robot Directory + US Certification

About

Cornelia Schulz is a robotics researcher whose work sits at the intersection of simultaneous localization and mapping (SLAM), probabilistic localization, and autonomous mobile systems. Her most significant contributions center on advancing Normal Distributions Transform (NDT) representations for robot mapping, including the development of efficient multi-dimensional map structures using indexed kd-trees — work that has collectively attracted over 20 citations and addresses critical challenges in both indoor and outdoor navigation for ground and aerial robots. Schulz's most-cited contribution, a real-time graph-based SLAM system leveraging Occupancy Normal Distributions Transforms (2020), demonstrates her ability to bridge theoretical map representations with practical autonomous navigation demands. Her innovative ARMCL framework applies Monte Carlo Localization principles to manipulator contact-point detection, broadening her influence into robotic manipulation and human-robot interaction. She has also explored multi-robot collaborative mapping under low-bandwidth constraints — a problem of high relevance to search-and-rescue applications — and developed sub-pixel ray-casting techniques to improve localization accuracy in coarse grid maps. More recently, Schulz has extended her autonomous systems expertise to glaciological field research, contributing to quad-polarimetric radar surveys in Antarctica. This interdisciplinary range marks her as a versatile researcher bridging robotics and environmental science.