Luka Sachße is a software engineer in the ANavS Computer Vision Team. He graduated in 2019 at TUM from the Geodesy and Geoinformation master’s program with a focus on Deep Learning based methods for Computer Vision and Remote Sensing. Since March 2020, he is working at ANavS where his main task is the integration of Visual SLAM / Odometry in the localization software of ANavS. He contributed to the development of vehicle and robot localization algorithms in multiple research projects.

Vaclav Navratil serves as assistant professor at the Czech Technical University in Prague, Faculty of Electrical Engineering. He is the current president of the Czech Institute of Navigation and a the Europe Regional Vice-Chair of CGSIC-IISC. He is focused primarily on positioning algorithms including sensor fusion, utilization of Signals of Opportunity and GNSS precise positioning techniques. He is also interested in RF measurements and instrumentation.

Paola Chicaiza received her MSc in Digital Signal Processing from Newcastle University, UK in 2017, graduating with distinction and first-class honours. She is currently a second-year PhD student at the PEMC Research Institute, University of Nottingham, UK, focusing on Unmanned Ground Vehicles operating in GNSS-denied environments, with an emphasis on collision avoidance and safety integrity. Her research integrates sensor fusion techniques using low-cost LiDAR and inertial sensors augmented by Machine Learning to enhance reliability and performance in challenging operational conditions.

Adam Kalisz is a researcher specializing in Visual Simultaneous Localization and Mapping (Visual SLAM) and sensor fusion technologies. He completed his Master’s degree in Computer Science at Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Germany, focusing on the integration of GNSS and camera-based systems. Following his graduation, Adam Kalisz worked as a researcher at the same institution for five years. During this time, his work concentrated on improving open-source Visual SLAM algorithms through comprehensive error modeling, as well as fusing diverse sensors such as GNSS, radar, LiDAR, IMU, and semantics to enhance localization accuracy.