# Experimental Analysis of the Behavior of Mirror-like Objects in LiDAR-Based Robot Navigation
## 1 Introduction  
Mirror-like objects are prevalent in modern buildings such as museums, offices, lobbies, and hospitals. Such reflective objects pose serious challenges to indoor autonomous navigation systems using simultaneous localization and mapping (SLAM) algorithms, aiming to represent the spatial environment (mapping) while keeping track of position (localization) within the built map. Since mapping and localization are highly correlated in SLAM, inaccurate maps caused by reflective objects adversely affect localization accuracy. SLAM frameworks use sensor technology for data acquisition, including acoustic, visual, and ranging sensors. To create a representation of the environment, occupancy grid maps provide a discretized representation of an environment where each of the grid cells is classified into two categories: occupied or free. Considering each cell as a binary variable, the map is able to estimate the location of an obstacle in the space by computing a posterior approximation for any given cell within the range of the sensor that is collecting data [1].  
Among various sensors, LiDAR sensors have received much attention in recent years due to their cost, high accuracy, long scanning range, and high stability [2]. LiDAR uses laser light to measure distance based on the reflective properties of the environment. The light is usually infrared, but can also be in the visible or ultraviolet range of the spectrum. LiDARs are advantageous in industry, as the collected data can be converted into 2D or 3D point clouds and easily integrated with other sensor data. Multiple consecutive LiDAR readings are used for complex applications such as obstacle detection, localization and mapping [3]. In addition, the width of the emitted beam can be made smaller to increase the distance it can travel, allowing LiDARs to detect obstacles over long ranges.  
However, there are some limitations associated with LiDARs. External sources that change visual properties affect LiDAR data. In outdoor applications, adverse weather, such as low-hanging clouds and heavy rain or fog, can have a negative impact on LiDAR data collection. One study was performed on ten different LiDARs in different weather conditions, including simulated fog, rain and intense sunlight [4]. LiDAR also has difficulty in some indoor applications. In indoor environments, the presence of walls, furniture, and other obstructions can block laser beams, making it difficult for LiDAR to generate a complete and accurate 3D representation of the environment. To overcome the line-of-sight limitations of LiDARs in indoor environments, fusion of sensors has been suggested in which LiDARs can be combined with other sensors, such as cameras, radar, and ultrasonic sensors, to provide a more complete representation of the environment. The data from these different sensors can be combined to produce a more comprehensive and accurate representation of the environment. SLAM algorithms can use the data collected by LiDAR at different locations to identify objects, even if they are not in the direct line of sight of the sensor. However, SLAM assumes that objects in the environment are diffuse objects [5]. Unlike opaque objects, reflective and transparent objects may lead to ambiguous or erroneous LiDAR perception processes. Mirror-like objects and glass-walled environments distort light beams. Due to data distortion, LiDAR sensors cannot accurately measure the distance to transparent and reflective objects, leading to a potential collision triggered by errors in SLAM map construction.