Semantic Monte-Carlo localization in changing environments using RGB-D cameras

Marian Himstedt, E. Maehle


The localization with respect to a prior map is a fundamental requirement for mobile robots. The commonly used adaptive monte carlo localization (AMCL) can be found on most of the mobile robots ranging from small cleaning robots to large AGVs. While achieving accurate pose estimates in static environments, this algorithm tends to fail in the presence of significant changes. Recently published extensions and alternatives to AMCL observe the environment over longer times while building complex spatio-temporal models. Our approach, in contrast, utilizes object recognition and prior semantic maps to enable robust localization. It exploits the fact that putative changes in the environment can be predicted based on prior semantic knowledge. Our system is experimentally evaluated in a warehouse environment being subject to frequent changes. This emphasizes the importance of our algorithm for challenging industrial applications.
Original languageEnglish
Title of host publication2017 European Conference on Mobile Robots (ECMR)
Number of pages8
Publication date01.09.2017
ISBN (Print)978-1-5386-1097-8
ISBN (Electronic)978-1-5386-1096-1
Publication statusPublished - 01.09.2017
Event2017 European Conference on Mobile Robots - Paris, France
Duration: 06.09.201708.09.2017


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