Talk title: Combining Feed-Forward Reconstruction with Classical Visual SLAM for Scale-Consistent Reconstruction

Abstract: Feed-forward reconstruction methods (MASt3R, VGGT and siblings) are impressive in their ability to estimate 3D structure and relative motion from batches of monocular images. In this talk I will discuss what we have learned in pairing classical visual motion tracking with these methods. We have identified that FF models are imprecise compared to well-engineered visual-inertial SLAM systems but that a combined approach with minimal post-processing can achieve building-scale reconstruction in real-time. I will also present how our proposed approach can be combined with semantic segmentation to build object and room-level scene graphs. In a second part of the talk, I will discuss field robotics research into lidar place recognition, multi-session SLAM to create per-tree inventories autonomously within the context of the 8 partner EU project DIGIFOREST.

Bio: Maurice Fallon is an Professor of Robotics at University of Oxford. He leads the Dynamic Robot Systems Group which focuses on perception, mapping and navigation and focuses on dynamic robots. Originally from Ireland, Maurice's PhD (on the topic of audio source tracking) is from University of Cambridge. He was a post doc and later a research scientist at MIT from 2008 to 2015 working on marine navigation as well as perception lead for MIT’s team in the DARPA Robotics Challenge. He has been a PI on several large UK and EU collaborative projects including deploying mapping system at Chernobyl nuclear power plant and exploring underground mines.

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