# Long-Term Localization using Semantic Cues in Floor Plan Maps Source: [Long-Term Localization using Semantic Cues in Floor Plan Maps]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:38:22.399Z Authors: Nicky Zimmerman, Tiziano Guadagnino, Xieyuanli Chen, Jens Behley, Cyrill Stachniss Published: 2022-10-04T08:30:45.000Z ## Abstract Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Global Localization on Grocery store 20 trajectories 3,500 sq. ft. space](): Global Localization; Grocery store 20 trajectories 3,500 sq. ft. space; Global Success Rate: 30. - [Pose Tracking on Grocery store 20 trajectories 3,500 sq. ft. space](): Pose Tracking; Grocery store 20 trajectories 3,500 sq. ft. space; Track Percentage: 25. ## Original sources and implementation [Original paper]() [PDF]()