Planetary Monocular Simultaneous Localization and Mapping
Planetary Monocular Simultaneous Localization and Mapping
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DOI：
10.1002/rob.21608
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发表时间：
2016-03-01
影响因子：
8.3
通讯作者：
Gao, Yang
中科院分区：
文献类型：
--
作者：
Bajpai, Abhinav;Burroughes, Guy;Gao, Yang
行星单目同步定位与建图 (PM-SLAM) 是一种用于行星探索的模块化单目 SLAM 系统。该方法结合了用于视觉感知的受生物学启发的视觉显着性模型（即语义特征检测），以提高在行星探索的挑战性操作环境中的鲁棒性。介绍了一种生成混合显着特征的新方法，使用基于点的描述符来跟踪视觉显着性模型的产品。跟踪的特征用于使用 SLAM 过滤器进行漫游器和地图状态估计，从而形成适合用于长距离自主（微）漫游器导航的系统，并克服行星漫游器固有的硬件限制。单目图像用作系统的输入，主要动机是降低系统复杂性并优化微型移动平台。本文列出了模块化 SLAM 系统的各个组件，然后使用来自行星和小行星自然场景生成实用程序 (PANGU) 的模拟数据以及来自西威特灵现场试验（由 STAR 实验室执行）和智利 SEEKER 现场试验（由欧洲航天局执行）的真实数据集来评估其性能比较。该系统作为一个整体被证明能够可靠地执行，通过使用侯显着性和加速鲁棒特征（SURF）描述符与扩展卡尔曼滤波器的组合观察到最佳性能，该系统在具有挑战性的现实数据集上比最先进的独立优化的视觉里程计定位系统具有更高的精度。
Planetary monocular simultaneous localization and mapping (PM-SLAM), a modular, monocular SLAM system for use in planetary exploration, is presented. The approach incorporates a biologically inspired visual saliency model (i.e., semantic feature detection) for visual perception in order to improve robustness in the challenging operating environment of planetary exploration. A novel method of generating hybrid-salient features, using point-based descriptors to track the products of the visual saliency models, is introduced. The tracked features are used for rover and map state-estimation using a SLAM filter, resulting in a system suitable for use in long-distance autonomous (micro)rover navigation, and the inherent hardware constraints of planetary rovers. Monocular images are used as an input to the system, as a major motivation is to reduce system complexity and optimize for microrover platforms. This paper sets out the various components of the modular SLAM system and then assesses their comparative performance using simulated data from the Planetary and Asteroid Natural Scene Generation Utility (PANGU), as well as real-world datasets from the West Wittering field trials (performed by the STAR Lab) and the SEEKER field trials in Chile (performed by the European Space Agency). The system as a whole was shown to perform reliably, with the best performance observed using a combination of Hou-saliency and speeded-up robust features (SURF) descriptors with an extended Kalman filter, which performed with higher accuracy than a state-of-the-art, independently optimized visual odometry localization system on a challenging real-world dataset.