首页|Data on Robotics Reported by Researchers at University of Rey Juan Carlos (Open Source Robot Localization for Nonplanar Environments)
Data on Robotics Reported by Researchers at University of Rey Juan Carlos (Open Source Robot Localization for Nonplanar Environments)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting originating in Madrid, Spain, by NewsRx jour nalists, research stated, "The operational environments in which a mobile robot executes its missions often exhibit nonflat terrain characteristics, encompassin g outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations." Financial supporters for this research include MCIN/AEI/10.13039/501100011033, E uropean Union (EU). The news reporters obtained a quote from the research from the University of Rey Juan Carlos, "This study delineates a localization framework incorporating grou nd elevation and incline considerations, deviating from traditional two-dimensio nal localization paradigms that may falter in such contexts. In our proposed app roach, the map encompasses elevation and spatial occupancy information, employin g Gridmaps and Octomaps. At the same time, the perception model is designed to a ccommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an i mplementation of our approach fully working with Nav2, ready to replace the base line Adaptative Monte Carlo Localization (AMCL) approach when the robot is in no nplanar environments. Our methodology was rigorously tested in both simulated en vironments and through practical application on actual robots, including the Tia go and Summit XL models, across various settings ranging from indoor and outdoor to flat and uneven terrains. Demonstrating exceptional precision, our approach yielded error margins below 10 cm and 0.05 radians in indoor settings and less t han 1.0 m in extensive outdoor routes. While our results exhibit a slight improv ement over AMCL in indoor environments, the enhancement in performance is signif icantly more pronounced when compared to three-dimensional simultaneous localiza tion and mapping algorithms."
MadridSpainEuropeEmerging Technolo giesMachine LearningNano-robotRobotRoboticsUniversity of Rey Juan Carl os
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