Martín Rico, FranciscoGuerrero Hernández, José MiguelPérez-Rodríguez, RodrigoPeña-Narvaez, Juan DiegoGarcía Gómez-Jacinto, Alberto2024-06-272024-06-272024-05-07Rico, F.M., Hernández, J.M.G., Pérez-Rodríguez, R., Peña-Narvaez, J.D. & Gómez-Jacinto, A.G. (2024) Open source robot localization for nonplanar environments. Journal of Field Robotics, 1–18. https://doi.org/10.1002/rob.223531556-4967 (online)1556-4959 (print)https://hdl.handle.net/10115/35425The authors also would like to thank Juan Carlos Manzanares, who helped us to carry out the experiments. This work is partially funded under Project PID2021-126592OB-C22 funded by MCIN/AEI/10.13039/501100011033, the grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR,” and by CORESENSE project with funding from the European Union's Horizon Europe Research and Innovation Programme (Grant Agreement No. 101070254)The operational environments in which a mobile robot executes its missions often exhibit nonflat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and incline considerations, deviating from traditional two-dimensional localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an implementation of our approach fully working with Nav2, ready to replace the baseline Adaptative Monte Carlo Localization (AMCL) approach when the robot is in nonplanar environments. Our methodology was rigorously tested in both simulated environments and through practical application on actual robots, including the Tiago 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 than 1.0 m in extensive outdoor routes. While our results exhibit a slight improvement over AMCL in indoor environments, the enhancement in performance is significantly more pronounced when compared to three-dimensional simultaneous localization and mapping algorithms. This underscores the considerable robustness and efficiency of our approach, positioning it as an effective strategy for mobile robots tasked with navigating expansive and intricate indoor/outdoor environmentsengAtribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/article10.1002/rob.22353info:eu-repo/semantics/openAccess