Abstract: To power the next generation mobile robots and drones, the field of spatial perception has made much progress from robust multi-sensor SLAM to dense, semantic, and object-level maps, with the aim of understanding open-ended environments as a basis for mobile robot navigation and environment interaction. I will show recent progress in reliable and real-time state estimation and 3D scene understanding using vision, LiDAR, IMUs, and more. Scenes to be reconstructed may contain dynamic objects and even people, whose poses, postures, and motions we can estimate in a tightly-coupled manner. In our works, we fully embrace the power of machine learning-based approaches, but typically integrated in modular, complex robotic systems that may include model-based methods as well. Our approaches are demonstrated as crucial enablers of a range of robot applications, from mobile manipulation on construction sites to drones exploring obstructed indoor spaces or flying through the forest.
Bio: Stefan is an Assistant Professor (Tenure Track) at the Technical University of Munich (TUM) in the School of Computation, Information and Technology (CIT) and has further affiliations with the Munich Institute of Robotics and Machine Intelligence (MIRMI) as well as the Munich Data Science Institute (MDSI) and the Munich Center for Machine Learning (MCML). He leads the Smart Robotics Lab (SRL) working at the intersection of perception, mobile robotics, drones, and machine learning. Stefan also still holds the position of a visiting Reader at the Department of Computing of Imperial College London, his previous post. He has also co-founded SLAMcore, a spin-out company aiming at commercialisation of localisation and mapping solutions for robots and drones. Stefan has received a BSc and from ETH Zurich, as well as a PhD on “Unmanned solar airplanes: design and algorithms for efficient and robust autonomous operation” in 2014.