Localization in the environment is an essential navigational capability for animals and mobile robots. In the indoor environment, the global localization problem remains challenging to be perfectly solved with probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, it is intriguing and promising to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based re-localization approach. The experiment results show that the proposed method is competitive to Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in different localization scenarios.
翻译：环境的本地化是动物和移动机器人的基本导航能力。在室内环境中,全球本地化问题仍然具有挑战性,难以用概率方法完全解决。然而,动物能够本能地本地化,但努力少得多。因此,寻求动物的生物灵感是令人感兴趣和充满希望的。在本文中,我们展示了一种由生物启发的全球本地化系统,它使用一个利用河马营模型和具有里程碑意义的重新本地化方法的LIDAR传感器。实验结果显示,拟议方法对蒙特卡洛本地化具有竞争力,结果显示,在不同本地化情景下,拟议中由生物启发的本地化系统具有高度的准确性、适用性和可靠性。
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