## 1 Introduction  
Real-time localization is one of the key techniques used in robotization, which estimates the location and orientation of a mobile robot within its operating environment by aligning data from a combination of sensors and information of the environment stored in a map. The accurate and robust calculation of the location, as well as orientation, in the map provides an essential basis for a robot to determine appropriate actions in given situations [1]. With the increasing utilization of indoor mobile robots, recent studies have presented various techniques to determine the robot poses within built environments [2; 3; 4]. In its most basic approach, a wheel-based mobile robot can estimate its position based on wheel encoders. Despite its simple implementation, pose estimation using a wheel encoder can suffer from drift. As another class of approaches, studies have presented methods that rely on landmarks (such as BLE sensors [5], RFID sensors [6], and visual markers [7]) installed in the environment. Even though these landmarks can explicitly indicate known locations, these approaches can also suffer significantly from even slight changes made to the configuration of the environment. Deviations from the initial setting, such as additional interior partitions and the relocation of large furniture and electronic appliances, dramatically reduce the accuracy, which then requires recalibration of the entire setting for localization. Many probabilistic methods were then proposed recently to allow for the estimation of possible robot poses by fusing data from multiple sensors [8]. Becauseof robustness in response to changes in the environment, probabilistic approaches have the potential to overcome the limitations of deterministic localization methods. Monte Carlo Localization (MCL) [9] continuously generates the distribution of arbitrary robot poses and selects samples from the particles of highly probable robot poses. This approach uses pose data, previously built occupancy grid maps, sensor data, and Kullback-Leibler divergence (KLD)-based sampling. Adaptive Monte Carlo Localization (AMCL) [10] is one of the most widely used variations derived from MCL. A Robot Operating System (ROS)-based implementation of AMCL [11] has been applied in many studies [12; 13] that successfully fused data from multiple sensors (e.g., 2D laser scan, IMU, wheel encoder) to accurately estimate robot poses.  
Despite these advances, most of the approaches in robot localization were developed with the assumption that the operating environment is static. A typical map reflecting the knowledge about the present environment is a two-dimensional grid map depicting unchanging structural elements, such as walls, floors, and ceilings. In a typical building environment, however, non-structural building elements and common items (e.g., temporarily loaded materials, doors, appliances, and furniture) also exist, which change location occasionally and consequently significantly impact the accuracy of localization [14]. In order to overcome the impact of these non-structural elements that are not being represented within a map, this study proposes a method that allows a mobile robot to continuously update the map with objects it encounters during navigation and correct possible positioning errors automatically. More specifically, we propose a modification to the traditional AMCL localization method to integrate image-based object recognition, point cloud processing, and map updating. Then, we evaluated the performance of the modified AMCL method based on a conventional localization scenario, where a robot starts the navigation from a known location, and also a kidnapped robot scenario, where a robot has to find its location without knowledge about prior locations.