## 4 Methodology  
### Overview of the Modified AMCL with Map Updating  
This section describes the overview of the proposed modified AMCL with real-time map updating. As shown in Figure 1, the proposed process is composed of three major components, including semantic building map creation, map updating with sensor-based object detection, and AMCL localization with the updated map. When localization or navigation begins, (1) **semantic building model creation** generates a 3D semantic building model and a 2D AMCL map that can be used by an autonomous robot. A BIM model, as a building lifecycle database, contains comprehensive geometric and non-geometric information about a building. A semantic building model in our study is a ROS-compliant building model that can describe both static elements (e.g., walls, floors, ceilings) and dynamic elements (e.g., doors, furniture, large electronic devices) in a way that can be interpreted by a robot. We utilized the IFC-to-URDF convertor used in our previous work [42; 43] that parses information in a BIM model and augments additional information to generate a semantic 3D building model. Based on the semantic 3D building model, a 2D AMCL map is generated as the essential input for AMCL-based localization and navigation. The semantic 3D building model is updated when a robot detects the presence of non-structural elements, such as doors or furniture, and estimates their poses within the building, and the 2D AMCL map is updated accordingly to reflect the observed changes. Depending on the characteristics of the objects, the 2D AMCL map can be directly updated without updating the 3D model. For example, the state of a swing door can be accurately described by updating the door angle around the known door hinge coordinate stored in the semantic 3D model. A 2D map showing the area occupied by the door can be generated based on the 3D model. In this way, an accurate 2D map can be created even with only partial observation of the elements. However, it is difficult to accurately estimate the poses and boundaries of non-building elements (such as a sofa, table, bed) without fully observing the entire element from all angles, which would require excessive additional movements. For non-building elements, our study estimates the partial boundary of the elements that can impact the accuracy of localization and updates the 2D AMCL map directly without changing the 3D model. However, accurately updating the 3D building model with non-building elements would also be possible if the information about the items could be provided in advance. (2) **Sensor-based object recognition** utilizes sensors attached to the mobile robot to detect the presences of objects that can impact localization and estimate their boundaries to be updated in the 2D/3D maps. From the RGB images, the robot detects and classifies objects using object-recognition techniques. Then, it acquires geometric information on the detected objects using depth point cloud data. The presence and states of the detected elements are then updated in the 2D/3D maps reflecting the current state of the environment. (3) **AMCL localization with an updated map** is a conventional localization method utilizing the given map of the environment. AMCL is a probability-based 2D robot localization algorithm, also known as particle filter localization, which uses many particles to track the pose of a robot in a known map with an adaptive (or KLD sampling) Monte Carlo localization approach. Each particle in the particle filter represents a distribution of possible states, considered a hypothesis of where the robot is. The regions with many particles indicate an increased probability of the robot's actual position, whereas regions with fewer particles are unlikely to be where the robot is. The algorithm usually initializes uniformly distributed random particles over the known map because the robot has no knowledge of its pose on start-up and assumes that it is equally likely to be anywhere on the known map.