{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:45:01Z","timestamp":1776357901086,"version":"3.51.2"},"reference-count":15,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Encoding sensor data into a map is a problem that must be undertaken by any robotic agent operating in unknown or uncertain environments, and real-time updates are crucial to safe planning and control. Most modern robotic sensors produce some form of depth data or point cloud information that is only useful to the agent after being processed into the appropriate data structure, oftentimes an occupancy map. However, as the quality of sensor technology improves, so does the magnitude of the input data, which can creates a problem when trying to construct occupancy maps in real-time. Populating such an occupancy map using these dense point clouds can quickly become an expensive process, and many robotic agents have limited onboard computational bandwidth and memory. This results in delayed map updates and reduced operational performance in dynamic environments where real-time information is crucial for safe operation. However, while many modern robotic agents are still relatively limited by the power of onboard central processing units (CPUs), many platforms are gaining access to onboard graphics processing units (GPUs), and these resources remain underutilised with respect to the problem of occupancy mapping. We propose a novel probabilistic mapping solution that leverages a combination of OpenVDB, NanoVDB, and Nvidia\u2019s Compute Unified Device Architecture (CUDA) to encode dense point clouds into OpenVDB data structures, leveraging the parallel compute strength of GPUs to provide significant speed advantages and further free up resources for tasks that cannot as easily be performed in parallel. An evaluation of our solution is provided, with performance benchmarks provided for both a laptop and a low power single board computer with onboard GPU. Similar performance improvements should be accessible on any system with access to a CUDA-compatible GPU. Additionally, our library provides the means to simulate one or more sensors on an agent operating within a randomly generated 3D-grid environment and create a live map for the purposes of evaluating planning and control techniques and for training agents via deep reinforcement learning. We also provide interface packages for the Robotic Operating System (ROS1) and the Robotic Operating System 2 (ROS2), and a ROS2 visualisation (RVIZ2) plugin for the underlying OpenVDB data structure.<\/jats:p>","DOI":"10.3390\/rs14215463","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"5463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["NanoMap: A GPU-Accelerated OpenVDB-Based Mapping and Simulation Package for Robotic Agents"],"prefix":"10.3390","volume":"14","author":[{"given":"Violet","family":"Walker","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Robotics, Queensland University of Technology, 2 George St., Brisbane, QLD 4000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1821-1263","authenticated-orcid":false,"given":"Fernando","family":"Vanegas","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics, Queensland University of Technology, 2 George St., Brisbane, QLD 4000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4342-3682","authenticated-orcid":false,"given":"Felipe","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics, Queensland University of Technology, 2 George St., Brisbane, QLD 4000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Collins, T., Collins, J., and Ryan, D. (2007, January 27\u201329). Occupancy grid mapping: An empirical evaluation. Proceedings of the 2007 Mediterranean Conference on Control & Automation, Athens, Greece.","DOI":"10.1109\/MED.2007.4433772"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vanegas, F., and Gonzalez, F. (2016). Enabling UAV navigation with sensor and environmental uncertainty in cluttered and GPS-denied environments. Sensors, 16.","DOI":"10.3390\/s16050666"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Buerkle, C., Oboril, F., Jarquin, J., and Scholl, K.U. (November, January 19). Efficient dynamic occupancy grid mapping using non-uniform cell representation. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304571"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sandino, J., Vanegas, F., Maire, F., Caccetta, P., Sanderson, C., and Gonzalez, F. (2020). UAV framework for autonomous onboard navigation and people\/object detection in cluttered indoor environments. Remote Sens., 12.","DOI":"10.3390\/rs12203386"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Serna, J.G., Vanegas, F., Gonzalez, F., and Flannery, D. (2020, January 7\u201314). A review of current approaches for UAV autonomous mission planning for Mars biosignatures detection. Proceedings of the 2020 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO47225.2020.9172467"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Niemand, J., Mathew, S.J., and Gonzalez, F. (2020, January 1\u20134). Design and testing of recycled 3D printed foldable unmanned aerial vehicle for remote sensing. Proceedings of the 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece.","DOI":"10.1109\/ICUAS48674.2020.9213961"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mandel, N., Sandino, J., Galvez-Serna, J., Vanegas, F., Milford, M., and Gonzalez, F. (2022, January 5\u201312). Resolution-adaptive quadtrees for semantic segmentation mapping in UAV applications. Proceedings of the 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA.","DOI":"10.1109\/AERO53065.2022.9843498"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","article-title":"OctoMap: An efficient probabilistic 3D mapping framework based on octrees","volume":"34","author":"Hornung","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"De Gregorio, D., and Di Stefano, L. (June, January 29). Skimap: An efficient mapping framework for robot navigation. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989299"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Besselmann, M.G., Puck, L., Steffen, L., Roennau, A., and Dillmann, R. (2021, January 23\u201327). VDB-Mapping: A High Resolution and Real-Time Capable 3D Mapping Framework for Versatile Mobile Robots. Proceedings of the 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France.","DOI":"10.1109\/CASE49439.2021.9551430"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1729881420910530","DOI":"10.1177\/1729881420910530","article-title":"Spatio-temporal voxel layer: A view on robot perception for the dynamic world","volume":"17","author":"Macenski","year":"2020","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jia, T., Yang, E.Y., Hsiao, Y.S., Cruz, J., Brooks, D., Wei, G.Y., and Reddi, V.J. (2022). OMU: A probabilistic 3D occupancy mapping accelerator for real-time OctoMap at the edge. arXiv.","DOI":"10.23919\/DATE54114.2022.9774508"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Museth, K., Lait, J., Johanson, J., Budsberg, J., Henderson, R., Alden, M., Cucka, P., Hill, D., and Pearce, A. (2013). OpenVDB: An open-source data structure and toolkit for high-resolution volumes. Acm Siggraph 2013 Courses, Proceedings of the SIGGRAPH\u2019 13: Special Interest Group on Computer Graphics and Interactive Techniques Conference, Anaheim, CA, USA, 21\u201325 July 2013, Association for Computing Machinery.","DOI":"10.1145\/2504435.2504454"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Museth, K. (2021). NanoVDB: A GPU-friendly and portable VDB data structure for real-time rendering and simulation. ACM SIGGRAPH 2021 Talks, Proceedings of the SIGGRAPH\u2019 21: Special Interest Group on Computer Graphics and Interactive Techniques Conference, Virtual Event, 9\u201313 August 2021, Association for Computing Machinery.","DOI":"10.1145\/3450623.3464653"},{"key":"ref_15","unstructured":"Sanders, J., and Kandrot, E. (2010). CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Wesley Professional."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5463\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:06:17Z","timestamp":1760144777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,30]]},"references-count":15,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215463"],"URL":"https:\/\/doi.org\/10.3390\/rs14215463","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,30]]}}}