Map Matching Performance under Various Similarity Metrics for Heterogeneous Robot Teams
Aerial and ground robots have various advantages of usage in different missions. Aerial robots can move quickly and get a different sight of view of the area, but those vehicles cannot carry heavy payloads. On the other hand, unmanned ground vehicles (UGVs) are slow moving vehicles, since those can carry heavier payloads than unmanned aerial vehicles (UAVs). In this context, we investigate the performances of various Similarity Metrics to provide a common map for Heterogeneous Robot Team (HRT) in complex environments. Within the usage of Lidar Odometry and Octree Mapping technique, the local 3D maps of the environment are gathered. In order to obtain a common map for HRT, informative theoretic similarity metrics are exploited. All types of these similarity metrics gave adequate as allowable simulation time and accurate results that can be used in different types of applications. For the heterogeneous multi robot team, those methods can be used to match different types of maps.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2022111Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 532
 L. E. Parker, B. Kannan, F. T. F. Tang, and M. Bailey, “Tightly-coupled navigation assistance in heterogeneous multi-robot teams,” 2004 IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IEEE Cat. No.04CH37566), vol. 1, pp. 1016–1022, 2004.
 M. Hofmeister, M. Kronfeld, and A. Zell, “Cooperative visual mapping in a heterogeneous team of mobile robots,” Proc. - IEEE Int. Conf. Robot. Autom., pp. 1491–1496, 2011.
 T. Bailey, M. Bryson, H. Mu, J. Vial, L. McCalman, and H. Durrant-Whyte, “Decentralised cooperative localisation for heterogeneous teams of mobile robots,” Proc. - IEEE Int. Conf. Robot. Autom., pp. 2859–2865, 2011.
 M. Langerwisch, T. Wittmann, S. Thamke, T. Remmersmann, A. Tiderko, and B. Wagner, “Heterogeneous teams of unmanned ground and aerial robots for reconnaissance and surveillance-a field experiment,” Safety, Secur. Rescue Robot. (SSRR), 2013 IEEE Int. Symp., pp. 1–6, 2013.
 Y. Ktiri and M. Inaba, “A framework for multiple heterogeneous robots exploration using laser data and MARG sensor,” 2012 IEEE/SICE Int. Symp. Syst. Integr. SII 2012, pp. 635–640, 2012.
 A. Husain et al., “Mapping planetary caves with an autonomous, heterogeneous robot team,” IEEE Aerosp. Conf. Proc., 2013.
 C. Forster, M. Pizzoli, and D. Scaramuzza, “Air-ground localization and map augmentation using monocular dense reconstruction,” IEEE Int. Conf. Intell. Robot. Syst., pp. 3971–3978, 2013.
 L. Kneip, M. Chli, and R. Y. Siegwart, “Robust Real-Time Visual Odometry with a Single Camera and an IMU.,” Bmvc, p. 16.1-16.11, 2011.
 R. Kaeslin et al., “Collaborative Localization of Aerial and Ground Robots through Elevation Maps,” IEEE Int. Symp. Safety, Secur. Map/Collaborative_Navigation_for_Flying_and_Walking_Robots_Marco_Hutter_2016.pdfy Rescue Robot., 2016.
 S. Cha, “Comprehensive Survey on Distance / Similarity Measures between Probability Density Functions,” vol. 1, no. 4, 2007.
 J. Zhang and S. Singh, “LOAM: Lidar Odometry and Mapping in Real-time,” 2014.
 A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “OctoMap: An efficient probabilistic 3D mapping framework based on octrees,” Auton. Robots, vol. 34, no. 3, pp. 189–206, 2013.
 M. Santana, K. R. T. Aires, and R. M. S. Veras, “An Approach for 2D Visual Occupancy Grid Map Using Monocular Vision,” vol. 281, pp. 175–191, 2011.