Carlos Medina Sánchez

@article{medina2022mdpi,

title = {From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art},

author = {Carlos Medina-Sánchez and Matteo Zella and Jesús Capitán and Pedro José Marrón},

url = {https://www.mdpi.com/1424-8220/22/3/1191},

doi = {10.3390/s22031191},

isbn = {1424-8220},

year  = {2022},

date = {2022-02-04},

journal = {Sensors},

volume = {22},

number = {3},

abstract = {Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Medina-Sanchez2020-1,

title = {Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments},

author = {Carlos Medina-Sánchez and Matteo Zella and Jesús Capitán and Pedro José Marrón},

url = {https://www.mdpi.com/2076-3417/10/20/7154},

doi = {https://doi.org/10.3390/app10207154},

year  = {2020},

date = {2020-10-14},

journal = {Appl. Sci.},

volume = {10},

number = {7154},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}