Abstract — Navigation information is an essential element for the operation of robotics platforms and intelligent transportation systems. Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, providing all-weather, all-time positioning and timing at a worldwide scale. The use of GNSS carrier phase pseudorange observations and multi-antenna configurations result key for high precision navigation, allowing to provide cm-level positioning and sub-degree attitude estimates. Doing so, however, requires dealing with estimation procedures over a mixture of real, integer and on-manifold parameters. Furthermore, to avoid the negative effects of signal reflection in urban scenarios, robust estimators are needed for the navigation problem. In this talk, insights to the mixed estimation problem and its bounds are presented alongside with the intricacies of robust estimation for achieving high precision navigation resilient to harsh propagation environments.
Bio — Daniel Medina obtained his B.S. in Electrical Engineering from the University of Malaga in 2014 and his M.S. on Computer Sciences in 2016 from Universidad Carlos III de Madrid. He recently obtained his PhD on the topic “Robust GNSS Carrier Phase-based Position and Attitude Estimation” from University Carlos III de Madrid. Since 2016, Daniel works in the Multi-Sensor Systems Group of the Institute of Communications and Navigation of DLR.