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Lifelong localization in a given map is an essential capability forautonomous service robots. In this paper, we consider the task of long-termlocalization in a changing indoor environment given sparse CAD floor plans. Thecommonly used pre-built maps from the robot sensors may increase the cost andtime of deployment. Furthermore, their detailed nature requires that they areupdated when significant changes occur. We address the difficulty oflocalization when the correspondence between the map and the observations islow due to the sparsity of the CAD map and the changing environment. Toovercome both challenges, we propose to exploit semantic cues that are commonlypresent in human-oriented spaces. These semantic cues can be detected using RGBcameras by utilizing object detection, and are matched against aneasy-to-update, abstract semantic map. The semantic information is integratedinto a Monte Carlo localization framework using a particle filter that operateson 2D LiDAR scans and camera data. We provide a long-term localization solutionand a semantic map format, for environments that undergo changes to theirinterior structure and detailed geometric maps are not available. We evaluateour localization framework on multiple challenging indoor scenarios in anoffice environment, taken weeks apart. The experiments suggest that ourapproach is robust to structural changes and can run on an onboard computer. Wereleased the open source implementation of our approach written in C++ togetherwith a ROS wrapper.

Long-Term Localization using Semantic Cues in Floor Plan Maps