# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
## 1 Introduction  
Imagine that in the future every building will look like a living organism comparable to a hive where both humans, robots, and other devices are going to cooperate on their tasks. For example, mobile robotic systems will assist people with basic repetitive tasks, such as package and food delivery, human transportation, security, cleaning, and maintenance. Besides fulfilling the purpose, robots need to be safe for any person that comes in contact with one, so any possible harm is avoided. A robot position tracking and collision avoidance plays a key role to ensure such capabilities. There are many methodologies for motion planning and their theoretical approaches are described in a survey by [PERSON] and [PERSON] [1]. One of the most used methods is Simultaneous Localization and Mapping (SLAM) [2] where the environment is not known, so a vision based system is placed on a robot to observe the area and create a map of it, eventually to perform trajectory planning on top of it. There were many enhancements of the SLAM methods proposed, some ofthem are compared by [PERSON] (2018) and the most common sensors used there are lidars (2018, 2019, 2019, 2018) and depth cameras (2018, 2019, 2019, 2019). However, when the environment is known (indoor areas frequently), the advanced SLAM systems may become redundant and if more robots are deployed, it becomes costly when every single robot has to carry all sensors. Therefore, some alternatives come to mind, such as real-time locating systems (RTLS), when devices working on the radio frequency communication principle are placed in the environment to determine the position of a robot. Under this technology, one can imagine WiFi (2018, 2019), when time-difference measurements between the towers and the monitored object are obtained, or RFID (2019, 2020, 2021), when tags shaped in a matrix on the floor or walls are detected by a transmitter on the robot. However, deployment of RTLS is not always possible, when WiFi needs uninterrupted visual contact and reliable communication between the towers. The RFID tags on the floor can be damaged during a time and the accuracy is based on the density (amount) of the tags. In general, RTLS is suitable only for indoor areas. On the other hand, the most common GNSS systems for outdoor navigation cannot provide a reliable solution in the case of indoor environments. Another approach for robot navigation is odometry, which analyzes the data from onboard sensors and estimates the position change over time. Mostly the wheel encoders are used (2019), or a camera for visual odometry (2019). Although, the odometry measurements have a disadvantage when they accumulate an error with time, as investigated by [PERSON] (2019), where he proposed a solution using a Kinect sensor that is detecting tag markers placed in the environment. Similar methods of robot localization when the map is known and visual markers are deployed are presented in (2019, 2019). The other possible solution of an object localization is a marker-based motion capture system, in which the functioning with multiple robots was demonstrated in (2019), but only a single camera was used and all paper-printed markers were the same, so it is not possible to use it for device identification. Infra-red markers have a similar disadvantage, they were used for tracking AGVs with either a number of high-speed cameras in (2019, 2020, 2021) or the Kinect system in (2021). When many robots at the same place are used, a field Swarm Robotics (2019) comes to mind. Some algorithms tested with real robots were introduced (2019, 2020), but they were not applied on different types of robots and have not been tested in cooperation with humans or in an environment where people randomly appear and disappear. Except for the RTLS and motion capture, the previously mentioned methods are based on sensor systems carried by a robot.