©The 2023 International Conference on Artificial Life and Robotics (ICAROB2023), Feb. 9 to 12, on line, Oita, Japan Modern Methods of Map Construction Using Optical Sensors Fusion Ramil Safin, Tatyana Tsoy, Roman Lavrenov, Ilya Afanasyev Department of Intelligent Robotics, Kazan Federal University, Kazan, Russia Evgeni Magid Department of Intelligent Robotics, Kazan Federal University, Kazan, Russia Higher School of Economics University, Moscow, Russia E-mail: safin.ramil@it.kfu.ru Abstract Map construction, or mapping, plays an important role in robotic applications. Mapping relies on inherently noisy sensor measurements to construct an accurate representation of a surrounding environment. Generally, individual sensors suffer from performance degradation issues under certain conditions in the environment. Sensor fusion allows to obtain statistically more accurate perception and to cope with performance degradation issues by combining data from multiple sensors of different modalities. This article reviews modern sensor fusion methods for map construction applications based on optical sensors, such as cameras and laser range finders. State-of-the-art mapping solutions built upon different mathematical theories and concepts, such as machine learning, are considered. Keywords: Sensor Fusion, Mapping, SLAM, Machine Learning, Camera, LiDAR 1. Introduction Mapping is the process of constructing a map of an environment using robot perception. There exist multiple map representations, such as sparse point clouds, topological maps, and dense voxel grids. Mapping could be difficult due to adverse conditions (e.g., low lightning) and presence of dynamic objects. Each type of sensor has its own limitations. For example, cameras underperform in low-light environments, while laser scanners cannot provide high-resolution data. To obtain reliable maps, it is required to combine strengths of each sensor to cope with their weaknesses. Sensor fusion, or data fusion, is a technique for combining data from multiple sensors in a way that allows to obtain more reliable and accurate information about the system being measured (Fig. 1). Data fusion is used in many robotics and machine vision applications, such as autonomous navigation and localization of mobile robots[1]. This article reviews modern sensor fusion methods for map construction applications based on optical sensors, such as cameras and laser scanners. State-of-the-art mapping solutions built upon different mathematical theories and concepts, such as machine learning, are considered. Fig. 1. An illustration of a laser range finder and camera sensor fusion. Uncertainty of the state is reduced due to multiple sources of information. 166