Continuity Risk of Feature Extraction for Laser-Based Navigation
Continuity Risk of Feature Extraction for Laser-Based Navigation
复制标题
激光导航特征提取的连续性风险
DOI：
10.33012/2017.14899
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
B. Pervan
中科院分区：
文献类型：
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作者：
M. Joerger;B. Pervan
在本文中，一个新的连续性风险评估方法的开发，模拟和测试的激光导航算法，使用特征提取（FE）和数据关联（DA）。FE和DA的主要安全风险是由不正确的关联引起的，当将从激光数据中提取的特征归因于预先建立的地图中的错误地标时，会发生这种情况。在之前的工作中，我们设计了一个基于创新的DA过程，以评估不正确的关联所造成的完整性风险，同时考虑所有潜在的测量排列。在本文中，置换再次使用在FE步骤，以确定提取的特征之间的最小归一化分离。分离不好的特征很容易找到，但很可能被错误地关联。如果最小间隔小于预期，则不提取特征，这导致导航连续性的损失。本文提供了一个分析上限的标称激光测量误差引起的连续性风险，并保证一个预定义的连续性水平的完整性风险界。这些安全风险界限进行了分析和测试的示例场景表明，较低的连续性风险的要求是，较高的完整性风险，由于不正确的关联变得。
In this paper, a new continuity risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). A major risk for safety in FE and DA is caused by incorrect association, which happens when attributing a feature extracted from laser data to the wrong landmark in a preestablished map. In prior work, we designed an innovation-based DA process to evaluate the integrity risk caused by incorrect associations while considering all potential measurement permutations. In this paper, permutations are used again at the FE step to determine the minimum normalized separation between extracted features. Features that are poorly separated are easily found, but are likely to be incorrectly associated. If the minimum separation is smaller than expected, then features are not extracted, which causes loss of navigation continuity. This paper provides an analytical upper-bound on the continuity risk caused by nominal laser measurement errors, and an integrity risk bound guaranteeing a predefined level of continuity. These safety risk bounds are analyzed and tested in example scenarios showing that the lower the continuity risk requirement is, the higher the integrity risk due to incorrect associations becomes.