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Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

Networked Embedded Systems Group, University of Duisburg Essen, 45127 Essen, Germany
Robotics, Vision and Control Group, University of Seville, 41092 Seville, Spain
Author to whom correspondence should be addressed.
Current address: Schützenbahn 70, 45127 Essen, Germany.
Appl. Sci. 2020, 10(20), 7154;
Received: 30 July 2020 / Revised: 30 September 2020 / Accepted: 8 October 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Cognitive Robotics)
The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding environment. Detecting and positioning objects as well as people in an accurate semantic map are, therefore, essential tasks that a robot needs to carry out. In this work, we walk an alternative path to build semantic maps of indoor scenarios. Instead of relying on high-density sensory input, like the one provided by an RGB-D camera, and resource-intensive processing algorithms, like the ones based on deep learning, we investigate the use of low-density point-clouds provided by 3D LiDARs together with a set of practical segmentation methods for the detection of objects. By focusing on the physical structure of the objects of interest, it is possible to remove complex training phases and exploit sensors with lower resolution but wider Field of View (FoV). Our evaluation shows that our approach can achieve comparable (if not better) performance in object labeling and positioning with a significant decrease in processing time than established approaches based on deep learning methods. As a side-effect of using low-density point-clouds, we also better support people privacy as the lower resolution inherently prevents the use of techniques like face recognition. View Full-Text
Keywords: semantic mapping; pointcloud-based sensors; service robots semantic mapping; pointcloud-based sensors; service robots
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MDPI and ACS Style

Sánchez, C.M.; Zella, M.; Capitán, J.; Marrón, P.J. Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments. Appl. Sci. 2020, 10, 7154.

AMA Style

Sánchez CM, Zella M, Capitán J, Marrón PJ. Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments. Applied Sciences. 2020; 10(20):7154.

Chicago/Turabian Style

Sánchez, Carlos M., Matteo Zella, Jesús Capitán, and Pedro J. Marrón 2020. "Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments" Applied Sciences 10, no. 20: 7154.

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