MAD-C: Multi-stage Approximate Distributed Cluster-combining for obstacle detection and localization

Agarwal, 2020 Alfano, 2003, Determining if two solid ellipsoids intersect, J. Guid. Control Dyn., 26, 106, 10.2514/2.5020 Ankerst, 1999, OPTICS: Ordering points to identify the clustering structure, 49 Ansari, 2019, Spatiotemporal clustering: a review, Artif. Intell. Rev., 1 Bentley, 1975, Multidimensional binary search trees used for associative searching, Commun. ACM, 18, 509, 10.1145/361002.361007 Cebrian, 2020, High-throughput fuzzy clustering on heterogeneous architectures, Future Gener. Comput. Syst., 106, 401, 10.1016/j.future.2020.01.022 Chiang, 2016, Fog and IoT: An overview of research opportunities, IEEE Internet Things J., 3, 854, 10.1109/JIOT.2016.2584538 Cormode, 2017, Data sketching, Commun. ACM, 60, 48, 10.1145/3080008 Djenouri, 2019, Exploiting GPU and cluster parallelism in single scan frequent itemset mining, Inform. Sci., 496, 363, 10.1016/j.ins.2018.07.020 Eisert, 1999, Multi-hypothesis, volumetric reconstruction of 3-D objects from multiple calibrated camera views, 3509 Elseberg, 2013, One billion points in the cloud - An octree for efficient processing of 3D laser scans, Int. J. Photogramm. Remote Sens., 76, 76, 10.1016/j.isprsjprs.2012.10.004 Ester, 1996, A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise, 226 Forman, 2000, Distributed data clustering can be efficient and exact, SIGKDD Explor., 2, 34, 10.1145/380995.381010 Fu, 2014, Online temporal-spatial analysis for detection of critical events in cyber-physical systems, 129 Galassi, 2009 2016 Geiger, 2013, Vision meets robotics: The KITTI dataset, Int. J. Robot. Res., 32, 1231, 10.1177/0278364913491297 Geiger, 2012, Are we ready for autonomous driving? The KITTI vision benchmark suite, 3354 Gohring, 2011, Radar/Lidar sensor fusion for car-following on highways, 407 Gulisano, 2015, Deterministic real-time analytics of geospatial data streams through scalegate objects, 316 Hansen, 2016 Havers, 2019, DRIVEN: a framework for efficient Data Retrieval and clustering in Vehicular Networks, 1850 Himmelsbach, 2010, Fast segmentation of 3D point clouds for ground vehicles, 560 Huebner, 2008, Minimum volume bounding box decomposition for shape approximation in robot grasping, 1628 Eshref Januzaj, Hans-Peter Kriegel, Martin Pfeifle, Towards effective and efficient distributed clustering, in: Workshop on Clustering Large Data Sets, ICDM, 2003, pp. 49–58. Kammerl, 2012, Real-time compression of point cloud streams, 778 Keramatian, 2018, MAD-C: Multi-stage approximate distributed cluster-combining for obstacle detection and localization, 312 Kohlhoff, 2019 Kumari, 2017, Exact, fast and scalable parallel DBSCAN for commodity platforms, 14:1 Leskovec, 2014 Michel, 2004, Cyberbotics Ltd. Webots: professional mobile robot simulation, Int. J. Adv. Robot. Syst., 1, 5, 10.5772/5618 H. Najdataei, Y. Nikolakopoulos, V. Gulisano, M. Papatriantafilou, Continuous and parallel LiDAR point-cloud clustering, in: 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS, 2018, pp. 671–684, July. Anh Nguyen, Bac Le, 3D point cloud segmentation: A survey, in: IEEE 6th International Conference on Robotics, Automation and Mechatronics, RAM 2013, Manila, Philippines, November 12–15, 2013, 2013, pp. 225–230. Oracle, 2019 M.M.A. Patwary, D. Palsetia, A. Agrawal, W. Liao, F. Manne, A. Choudhary, A new scalable parallel DBSCAN algorithm using the disjoint-set data structure, in: SC ’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2012, pp. 1–11, Nov. Powers, 2011, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, J. Mach. Learn. Technol. N. Preguica, J.M. Marques, M. Shapiro, M. Letia, A commutative replicated data type for cooperative editing, in: 2009 29th IEEE International Conference on Distributed Computing Systems, 2009, pp. 395–403, June. Richerzhagen, 2018, Better together: Collaborative monitoring for location-based services, 14 Rusinkiewicz, 2001, Efficient variants of the ICP algorithm, 145 Rusu, 2010, Semantic 3D object maps for everyday manipulation in human living environments, KI - Künstliche Intell., 24, 345, 10.1007/s13218-010-0059-6 R.B. Rusu, S. Cousins, 3D is here: Point cloud library (PCL), in: 2011 IEEE International Conference on Robotics and Automation, 2011, pp. 1–4, May. Schwarz, 2010, LIDAR: Mapping the world in 3D, Nat. Photonics, 4, 429, 10.1038/nphoton.2010.148 Sualeh, 2019, Dynamic multi-LiDAR based multiple object detection and tracking, Sensors, 19, 1474, 10.3390/s19061474 Tanenbaum, 2007 Wagner, 2007 Zimbelman, 2017, Hazards in motion: Development of mobile geofences for use in logging safety, Sensors, 17, 822, 10.3390/s17040822