Abstract. In this paper, we present a mapping system based on an autonomous mobile robot equipped with a LiDAR device and a camera, that can deal with the presence of people. Thanks to a deep learning approach, the position of humans is identified and a new surveying path is planned that brings the robot to scan occluded areas, so as to obtain a complete point cloud of the environment. Experimental results are performed with a wheeled mobile robot in different crowded scenarios, showing the applicability of the proposed approach to perform an autonomous survey avoiding occlusions and automatically removing from the map noisy and spurious objects caused by people presence.

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In this paper, we propose a comparison of open-source LiDAR and Inertial Measurement Unit (IMU)-based Simultaneous Localization and Mapping (SLAM) approaches for 3D robotic mapping. The analyzed algorithms are often exploited in mobile robotics for autonomous navigation but have not been evaluated in terms of 3D reconstruction yet. Experimental tests are carried out using two different autonomous mobile platforms in three test cases, comprising both indoor and outdoor scenarios. The 3D models obtained with the different SLAM algorithms are then compared in terms of density, accuracy, and noise of the point clouds to analyze the performance of the evaluated approaches. The experimental results indicate the SLAM methods that are more suitable for 3D mapping in terms of the quality of the reconstruction and highlight the feasibility of mobile robotics in the field of autonomous mapping.

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