1 code implementation • 23 Mar 2022 • Daniel Dugas, Olov Andersson, Roland Siegwart, Jen Jen Chung
In order to successfully solve the navigation task from only images, algorithms must be able to model the scene and its dynamics using only this channel of information.
1 code implementation • 8 Dec 2020 • Daniel Dugas, Juan Nieto, Roland Siegwart, Jen Jen Chung
In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation.
2 code implementations • 27 Sep 2019 • Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Hannes Sommer, Marcin Dymczyk, Juan Nieto, Roland Siegwart, Cesar Cadena
We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.
1 code implementation • 25 Apr 2018 • Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Juan Nieto, Roland Siegwart, Cesar Cadena
While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.
2 code implementations • 25 Sep 2016 • Renaud Dubé, Daniel Dugas, Elena Stumm, Juan Nieto, Roland Siegwart, Cesar Cadena
We propose SegMatch, a reliable loop-closure detection algorithm based on the matching of 3D segments.
Robotics