# Evaluating the Robot Inclusivity of Buildings Based on Surface Unevenness
## 1 Introduction  
There has been a significant rise in the deployment of autonomous mobile robots in indoor environments, encompassing multiple applications such as inspection [1], cleaning [2], logistics [3], and assistance applications [4]. Advancements in robot technologies and artificial intelligence have brought about a significant change in the way repetitive, time-consuming, and exhaustive tasks are performed by humans [5]. In these varied applications, navigation is a pivotal function undertaken by robots [6, 7]. Mapping and localization are essential for effective navigation [8, 9]. Mapping involves the creation of spatial representations of the robot's environment, enabling it to determine obstacles and plan paths for goals. Meanwhile, localization is crucial for the robot to determine its precise position within the mapped environment [10, 11]. This relationship between mapping and localization ensures accurate and reliable navigation, enabling robots to move around indoor spaces to accomplish tasks efficiently. Moreover, the significance of localization extends beyond navigation; it directly influences the quality and efficacy of mapping efforts, as precise localization facilitates the creation of detailed and reliable maps essential for successful robotic operations in diverse applications [12, 13]. Thus, within autonomous robotics, the synergy between navigation, mapping, and localization is crucial for achieving robust and adaptive performance in diverse indoor environments. Sensor fusion is the main method of improving localization accuracy in robotic systems [14, 15]. Various sensors such as Light Detection and Ranging (LiDAR), InertialMeasurement Unit (IMU), and wheel encoders are fused together for improving the localization accuracy of indoor robots [15]. LiDAR sensors visualize the robot's surroundings in the form of point cloud data [16], while wheel encoders send information related to the distance navigated by the robot, and IMU provides information on the robot's direction, acceleration, and angular velocity [17]. In addition to LiDAR, an optical camera can also be used to sense the robot's surroundings to create a map and measure the depth of the object from the robot's base [18]. The fusion algorithms combine the data from these sensors to compensate for the errors in each sensor to develop a reliable localization system, improving accuracy and reducing uncertainty. A few common sensor fusion techniques used for localization are particle filters [19] and Kalman filters [20]. Despite advanced sensor fusion methods and algorithms, there is still the probability for failure in accurate localization and mapping. Unevenness on surfaces like rugged or sudden steps can lead to imprecise sensor measurements. Uncertainty in the robot's deployment space can lead to misalignment in sensors' positions. This, in turn, affects the robot's localization and its navigation performance, leading to the robot's failure in performing the given tasks [21; 22]. The reliability of localization can also be improved by integrating the additional sensors into the robots' hardware or hybridizing multiple sensor inputs together [23]. Furthermore, artificial intelligence techniques such as deep learning methods [24] and reinforcement techniques [25] are explored to enhance a robot's localization accuracy. Even with advanced algorithms and the addition of the latest sensors to improve localization accuracy, there is still a chance of failure or inaccurate localization due to uneven surfaces. Furthermore, the prolonged operation of robots on uneven surfaces increases the degradation or failure of a robot's hardware system [26]. There should be a balance struck between improvements in the hardware and software of robots along with changes to be made to the infrastructure or environmental conditions where the robot is deployed [27; 28].