An ant-based filtering random-finite-set approach to simultaneous localization and mapping.

*(English)*Zbl 06960397Summary: Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.

##### Keywords:

simultaneous localization and mapping (SLAM); random finite sets; probability hypothesis density; ant colony
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\textit{D. Li} et al., Int. J. Appl. Math. Comput. Sci. 28, No. 3, 505--519 (2018; Zbl 06960397)

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##### References:

[1] | Adams, M., Vo, B., Mahler, R. and Mullane, J. (2014). SLAM gets a PHD: New concepts in map estimation, IEEE Robotics and Automation Magazine 21(2): 26-37. |

[2] | Aghdam, M., Ghasem-Aghaee, N. and Basiri, M. (2009). Text feature selection using ant colony optimization, Expert Systems with Applications 36(3): 6843-6853. |

[3] | Bailey, T., Nieto, J., Guivant, J., Stevens, M. and Nebot, E. (2006). Consistency of the EKF-SLAM algorithm, Proceedings of the 2006 IEEE International Conference on Intelligent Robots and Systems, Beijing, China, pp. 3562-3568. |

[4] | Carlevaris-Bianco, N., Kaess, M. and Eustice, R.M. (2017). Generic node removal for factor-graph SLAM, IEEE Transactions on Robotics 30(6): 1371-1385. |

[5] | Davison, A., Reid, I., Molton, N. and Stasse, O. (2007). MonoSLAM: Real-time single camera SLAM, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6): 1052-1067. |

[6] | Deusch, H., Reuter, S. and Dietmayer, K. (2015). The labeled multi-Bernoulli SLAM filter, IEEE Signal Processing Letters 22(10): 1561-1565. |

[7] | Dissanayake, G. and Durrant-Whyte, P., Clark, H. and Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem, IEEE Transactions on Robotic and Automation 17(3): 229-241. |

[8] | Dorigo, M. and Gambardella, L. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation 1(1): 53-66. |

[9] | Dorigo, M., Maniezzo, V. and Colorni, A. (1995). The ant system: Optimization by a colony of cooperative agents, Physical Review Letters 75(14): 2686-2689. |

[10] | Huang, H., Huang, C. and Pei, W. (2015). Solving multi-resource constrained project scheduling problem using ant colony optimization, Journal of Engineering Project and Production Management 5(1): 2-12. |

[11] | Leonard, J.J. and Durrant-Whyte, H.F. (1991). Simultaneous map building and localization for an autonomous mobile robot, Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems IROS’91, Osaka, Japan, pp. 1142-1147. |

[12] | Mahler, R. (2007). Statistical Multi-Source Multi-Target Information Fusion, Artech House, Boston, MA. |

[13] | Michael, M., Sebastian, T., Daphne, K. and Ben, W. (2003). FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges, Proceedings of the 6th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 1151-1156. |

[14] | Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B. (2002). FastSLAM: A factored solution to the simultaneous localization and mapping problem, Proceedings of the 8th National Conference on Artificial Intelligence, Edmonton, Alberta, Canada, pp. 593-598. |

[15] | Mullane, J., Vo, B.N., Adams, M.D. and Vo, B.T. (2011). A random-finite-set approach to Bayesian SLAM, IEEE Transactions on Robotics 27(2): 268-282. · Zbl 1231.68017 |

[16] | Narasimha, K., Kivelevitch, E., Sharma, B. and Kumar, M. (2013). An ant colony optimization technique for solving min-max multi-depot vehicle routing problem, Swarm and Evolutionary Computation 13: 63-73. |

[17] | Rashno, A., Sadri, S. and Sadeghian Nejad, H. (2015). An efficient content-based image retrieval with ant colony optimization feature selection schema based on wavelet and color features, Proceedings of the 2015 International Symposium on Artificial Intelligence and Signal Processing, Mashhad, Iran, pp. 59-64. |

[18] | Rodriguez-Losada, D., Matia, F., Pedraza, L., Jimenez, A. and Galan, R. (2007). Consistency of SLAM-EKF algorithms for indoor environments, Journal of Intelligent and Robotic Systems 50(4): 375-397. · Zbl 1133.68457 |

[19] | Shang, G. (2008). Solving weapon-target assignment problems by a new ant colony algorithm, Proceedings of the 2008 IEEE International Symposium on Computational Intelligence and Design, Wuhan, China, pp. 221-224. |

[20] | Taketomi, T., Uchiyama, H. and Ikeda, S. (2017). Visual SLAM algorithms: A survey from 2010 to 2016, IPSJ Transactions on Computer Vision & Applications 9(1): 16-26. |

[21] | Thrun, S., Burgard, W. and Fox, D. (2005). Probabilistic Robotics, MIT Press, Cambridge, MA. · Zbl 1081.68703 |

[22] | Viejo, D., Garcia-Rodriguez, J. and Cazorla, M. (2014). Combining visual features and growing neural gas networks for robotic 3D SLAM, Information Sciences 276: 174-185. |

[23] | Wang, H., Huang, S., Frese, U. and Dissanayake, G. (2013). The nonlinearity structure of point feature SLAM problems with spherical covariance matrices, Automatica 49(10): 3112-3119. · Zbl 1358.93192 |

[24] | Wilkowski, A., Kornuta, T., Stefa´nczyk, M. and Kasprzak, W. (2016). Efficient generation of 3D surfel maps using RGB-D sensors, International Journal of Applied Mathematics and Computer Science 26(1): 99-122, DOI: 10.1515/amcs-2016-0007. · Zbl 1336.94012 |

[25] | Xu, B., Chen, Q., Zhu, J. and Wang, Z. (2010). Ant estimator with application to target tracking, Signal Processing 90(5): 1496-1509. · Zbl 1194.94152 |

[26] | Xu, B., Xu, H. and Zhu, J. (2011). Ant clustering PHD filter for multiple-target tracking, Applied Soft Computing 11(1): 1074-1086. |

[27] | Zhang, H. (2015). Ant colony optimization for multimode resource-constrained project scheduling, Journal of Management in Engineering 28(2): 150-159. |

[28] | Zhang, Z., Gao, C., Lu, Y., Liu, Y. and Liang, M. (2016). Multi-objective ant colony optimization based on the Physarum-inspired mathematical model for bi-objective traveling salesman problems, PloS ONE 11(1): e0146709. |

[29] | Zhu, J., Xu, B., Wang, F. and Wang, Z. (2010). A real-time moving ant estimator for bearings-only tracking, Proceedings of the 1st International Conference on Advances in Swarm Intelligence, Beijing, China, pp. 273-280. |

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