# Automated Destination Renewal Process for Location-Based Robot Erands
## 2 Related Works  
In household chores and large-scale logistics services, advanced technologies integrated with robots are necessary. Particularly amid the increasing interest in Logistics 4.0, significant research is needed for logistics robots and domestic service robots to intelligently provide public services. In [21], [PERSON] and [PERSON] mention several challenges that need to be addressed, including intelligent transportation systems, to introduce logistics automation robots that constitute Logistics 4.0. [PERSON] [22] also notes that the autonomy enabled using STs (Smart Technologies) is considered a major trend in the logistics and transportation sectors in the near future, gradually replacing traditional processes with these technologies. Specifically, for a mobile robot to estimate its location and perform autonomous driving to its destination, the map-building must be carried out in advance, and a representative mapping method is SLAM. However, this method fundamentally requires the time-consuming part of searching the entire indoor environment to remove blind spots to set a travel destination and drive, and the efficiency of searching for unknown areas has fundamental problems such as cumulative errors due to real-time sensor measurement, dimensional distortion due to large-scale map creation, and above all, it takes a lot of time for developers to do preliminary work [23]. Loop closure detection is used to reduce the distortion of these accumulated errors [24], and lidar-based loop closure detection aligns maps by comparing previously collected point clouds with recent point clouds. However, in a typical loop closure, error compensation is performed using only the relative pose constraints of the nodes, so there is a limit to error reduction. To address this, [PERSON] et al., fuse odometer and laser sensor data to correct position errors [25]. Also, the robot's position was corrected by fusing the two types of sensor data using a piece-wise linear interpolation method, and the distortion problem was solved by converting the sensor data into a corrected coordinate system. When a mobile robot performs map-building, it moves carefully to reduce errors in sensor data. Therefore, map-building usually takes a lot of time. [PERSON] et al., present a mapping solution that included sensor movements in the observation model by considering the dynamics of the system using measurement data and position errors in high-speed moving map-building [26]. [PERSON] et al., use the absolute posture constraints of the mobile robot by taking advantage of the fact that the architectural floor plan accurately describes the environment [27]. In addition to SLAM methods that make use of floor plans, there exists an approach that directly employs the floor plan for robot localization [28; 29; 30; 31; 32; 33]. In [28], this localization method entails estimating the robot's location through image and drawing matching. Subsequently, trilateration using received signal strength indicator measurements is executed to rectify location errors, particularly in large-scale environments with similar layouts. For example, [PERSON] et al. [29] presented a method to perform localization by automatically recognizing the floor plan of the mobile robot itself without a separate drawing input and by comparing room plates and corners with measured ultrasonic sensor data as landmarks. [PERSON] et al. [30] provided a drawing as data for a mobile robot and then estimated the current location of the mobile robot by comparing the drawing with the recognized environment using lidar measurement. In this manner, the need for comparative measurements arises due to discrepancies between drawings and actual environmental data. However, research on updating environmental information based on drawings offers a straightforward means to identify indoor structures, enhancing navigation effectiveness and reducing errors in sensor data. Moreover, it provides the advantage of alleviating the workload and saving time for developers.