Three‐dimensional SLAM for mapping planetary work site environments
Three‐dimensional SLAM for mapping planetary work site environments
复制标题
用于绘制行星工作现场环境的三维 SLAM
DOI：
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发表时间：
2012
期刊：
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通讯作者：
E. Dupuis
中科院分区：
文献类型：
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作者：
Chi Hay Tong;T. Barfoot;E. Dupuis
在本文中，我们提出了一个强大的框架，适用于进行三维同步定位和映射（3D SLAM）在行星的工作现场环境。在行星环境中的操作施加了传感限制，以及由于崎岖地形而带来的挑战。利用安装在漫游车平台上的激光测距仪，我们已经展示了一种能够创建全球一致的自然，非结构化3D地形地图的方法。本文提出的框架利用基于稀疏特征的方法，并使用特征星座和密集数据的组合进行数据关联。由于特征稀缺，里程测量也被纳入，以提供特征贫乏地区的额外信息。为了保持全局一致性，使用批量对齐算法来解析这些测量，该批量对齐算法通过异构离群值拒绝来加强，以提高其对任一测量类型中的离群值的鲁棒性（即，激光或测距法）。最后，从对准估计和密集数据创建地图。使用在两个不同的行星模拟设施，其中包括50和102三维扫描，分别收集的数据提供了广泛的验证框架。在这些部位，标测误差的均方根分别为4.3 cm和8.9 cm。相对指标用于定位精度和地图质量，这有助于详细分析性能，包括故障模式和未来可能的改进。© 2012 Wiley Periodicals，Inc.
In this paper, we present a robust framework suitable for conducting three‐dimensional simultaneous localization and mapping (3D SLAM) in a planetary work site environment. Operation in a planetary environment imposes sensing restrictions, as well as challenges due to the rugged terrain. Utilizing a laser rangefinder mounted on a rover platform, we have demonstrated an approach that is able to create globally consistent maps of natural, unstructured 3D terrain. The framework presented in this paper utilizes a sparse‐feature‐based approach and conducts data association using a combination of feature constellations and dense data. Because of feature scarcity, odometry measurements are also incorporated to provide additional information in feature‐poor regions. To maintain global consistency, these measurements are resolved using a batch alignment algorithm, which is reinforced with heterogeneous outlier rejection to improve its robustness to outliers in either measurement type (i.e., laser or odometry). Finally, a map is created from the alignment estimates and the dense data. Extensive validation of the framework is provided using data gathered at two different planetary analogue facilities, which consist of 50 and 102 3D scans, respectively. At these sites, root‐mean‐squared mapping errors of 4.3 and 8.9 cm were achieved. Relative metrics are utilized for localization accuracy and map quality, which facilitate detailed analysis of the performance, including failure modes and possible future improvements. © 2012 Wiley Periodicals, Inc.