Hormann, Kai Giusti, Alessandro Guzzi, Jérôme Benini, Luca Zimmerman, Nicky 2024 Localization in a given map is an essential capability of most autonomous robots, and robust long-term localization is crucial in the case of service robots. This is a challenging task, especially in a dynamic, human-occupied environment, and it motivates the use of sparse map representations containing structural elements that remain constant over time. Floor plans, in particular, are a sparse map representation that is often readily-available without the additional cost and effort of sensor-based mapping. We present different strategies for achieving robust long-term localization in floor plans, by taking inspiration from the way humans navigate in indoor environments. We start with a classical range sensor-based particle filter framework and augment it by integrating textual in- formation. We then improve localization by considering a variety of semantic cues and propose a 3D metric-semantic map representation that enriches floor plans with semantic information. We address the challenge of localization on resource-constrained platforms and verify that our semantic localization approach is suitable for a variety of robotic platforms. Finally, we explore the benefits of collaborative localization, where robots in a team assist each other in improving the pose estimation. application/pdf https://susi.usi.ch/global/documents/329703 https://n2t.net/ark:/12658/srd1329703 https://susi.usi.ch/documents/329703/files/2024INF013.pdf eng info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:rero-006-122451 info:eu-repo/semantics/altIdentifier/ark/12658/srd1329703 info:eu-repo/semantics/openAccess License undefined Localization Mapping Robotics info:eu-repo/classification/udc/004 Long-term indoor localization in floor plans using semantic cues : for robot autonomy in human-oriented environments http://purl.org/coar/resource_type/c_db06