## 5 Case Studies: Localization in a Real-World Indoor Environment  
In this section, we present two case studies performed to demonstrate how the accuracy and robustness of indoor robot localization can be enhanced by allowing a mobile robot to recognize non-structural elements and update the map during the navigation. Case studies were conducted in a furnished apartment (Figure 9a) using a mobile robot called Turtlebot2 (Figure 9b). Based on the Kobuki mobile base, an Orbbec Astra camera and Slamtec RPLidar were installed for object recognition and localization, respectively. The Orbbec Astra camera collects sensor messages including RGB image, depth images, and the point cloud, which are required as input for YOLO-based object recognition and point cloud processing. The Slamtec RPLidar collects 360-degree 2D LiDAR scans that are required  
Figure 8: Example of object recognition and navigation map updating. (**a**) Robot observing a sofa in a Gazebo simulation, (**b**) robot’s internal state before map updating, and (**c**) robot’s internal state after map updating.  
to match the current location with locations in the 2D AMCL map. The living space is a 100 m\({}^{2}\) (1076 ft\({}^{2}\)) residential apartment that contains floors made of wood and marble. The doors in the apartment are mostly single doors with an average width of 71 cm (28 in). These widths, along with the hallway widths, are large enough for the TurtleBot2 to navigate without any issue. There is one set of double doors separating the dining and foyer areas, which is used for a portion of the case study.  
The case studies included both traditional localization (case study 1) and kidnapped robot (case study 2) scenarios. At the beginning of case study 1, as shown in Figure 10a, the robot first navigated the apartment along a predefined path from station 1 to station 10 and updated the original unaltered map with objects recognized by the Astra RGBD camera. By performing map updating, the robot was expected to correct errors caused by non-structural elements and adjust its XY location and orientation automatically. Then, we compared how the accuracy was impacted when the robot without map-updating capability utilized the unaltered original map as the present state-of-practice. Similarly, in case study 2, we compared the time and accuracy of localization when different maps (original unaltered map and the map updated from case study 1) were used in kidnapped robot situations. As shown in Figure 10b, the robot was carried to arbitrary locations (five stations) by a human. Even though the initial belief about the current pose was in office 2, the robot was instructed to rotate at its unknown locations to recover the current pose by matching the 2D LiDAR scans with the given maps. In these two case studies, the types of non-building objects were limited to doors and sofa. However, other common items can be included in practical implementations. One important aspect when considering localization is parameter setting to configure the AMCL algorithm. As shown in Table 1, the values of the AMCL parameters were set differently for traditional localization and kidnapped robot scenarios. For the kidnapped robot, a larger number of particles was generated (min_particle and max_particles), spread farther from the arbitrary location (init_cov_xx, init_cov_yy, and init_cov_aa), and converged slower (resample_interval, recovery_alpha_slow, and recovery_alpha_fast) to explore and search potential locations all over the building.  
Figure 9: (**a**) Furnished apartment for experiment and (**b**) Turtlebot2 with RGBD and 2D LiDAR sensors.