## 6 Conclusions and Discussion  
Accurate localization is an essential component in developing a robot-based processes in a built environment. Creating and updating a map of the environment plays an important role in accurately estimating its own location. However, most of the conventional robot localization methods have grounded their approaches on an assumption that the environments are static without considerations about non-structural elements not presented in the map negatively impacting the accuracy and robustness of localization. In order to address this limitation, our study proposed an AMCL-based indoor robot localization method, improved in terms of accuracy and robustness, which comprises combined object recognition and semantic three-dimensional building map dynamical updating. In this approach, (1) A 2D map generated based on BIM is provided to the mobile robot, and it navigates it with the SLAM algorithm using that information, (2) a mobile robot moves around an indoor environment to collect 3D point cloud data with a depth camera and extracts semantic object information that has not existed in the BIM model, and (3) the extracted new object information is updated and reflected in the two-dimensional map so that it can be used to improve the accuracy of localization and possibly for path planning, mission planning, and construction management process. By performing case studies with both traditional localization and kidnapped robot scenarios, this study proved that the map-updating capability embedded in AMCL localization could enhance the accuracy and robustness of a mobile robot's indoor localization. In the given environment, a mobile robot could accurately update a map of the indoor environment with non-structural objects using sensors, and importantly, by utilizing a continuously updated indoor map, accuracy and robustness could be enhanced in both traditional navigation and kidnapped localization scenarios. Moreover, by performing localization without map updating, this study found that indoor localization is significantly impacted by how non-structural elements are presented in a robot's map for the operating environment. Our study is distinguishable from existing approaches mainly because of the capability to detect non-structural elements during localization. We present a new process that detects non-structural elements with sensors and incorporates them into a semantic building map. Unlike conventional metric maps, the semantic 3D map of the building allows for a precise presentation of elements with their parameters (such as doors) instead of simple 2D shapes. From the 3D map, we directly generated 2D metric maps that are inputted for AMCL-based localization. By updating the maps in real-time, our approach allowed the robot to correct errors caused by non-structural elements without input from human users. However, there are several limitations that will lead to future studies. During the map updating, only the front boundaries of the objects were detected and inserted into the map. Even though this allowed for the real-time correction of localization errors, the estimation of exact poses of the elements was not achieved in this project. In future studies, exact poses of various types of elements can be calculated by preparing an inventory of elements \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Map** & **Updated Map** & **Stations** & **Original Map** & **Updated Map** \\ \hline  & & 2 & Fail & 35.5 s \\ \cline{2-4}  & & 3 & Fail & Fail \\ \cline{2-4}  & & 4 & Fail & 52.0 s \\ \cline{2-4}  & & 5 & Fail & Fail \\ \cline{2-4}  & & 6 & 18.2 s & 12.1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of localization at stations 2–6, with the average time in seconds before accurate localization. in the environment. A 3D point cloud of the detected object can be aligned to point cloud templates of objects in the inventory so that the robot can fully understand the states of the elements with partial observations.