Feature extraction and scene interpretation for map-based navigation and map building
Feature extraction and scene interpretation for map-based navigation and map building
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DOI：
10.1117/12.299565
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发表时间：
1998-01
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影响因子：
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通讯作者：
K. Arras;R. Siegwart
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文献类型：
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作者：
K. Arras;R. Siegwart
提出了一种从一维距离像数据中提取环境特征及其解译的方案。分割是通过确定模型保真度的度量来完成的，该度量应用于相邻的测量组。提取过程被认为包括随后的匹配步骤，其中属于同一地标的段被合并，同时跟踪那些源自不同特征的段。这是通过具有马氏距离矩阵的凝聚层次聚类算法来完成的。讨论了用极坐标求出广义最小二乘意义下的直线段及其一阶协方差估计的方法。因此，提取不再是单个范围读数级别上的实时问题，而必须在整个扫描级别上处理。给出了三台商用激光扫描仪的实验结果。在一个基于地图定位的移动机器人上的实现证明了该方法在实时条件下的准确性和适用性。从提取过程获得的线段和相关协方差矩阵的集合包含比基于地图的定位所需的更多关于场景的信息。在随后的推理步骤中，该信息是明确的。通过对不确定性的逐次抽象和后续传播，最终得到一个紧凑的场景模型，该模型以加权符号描述的形式保存了拓扑信息，并反映了局部观测的主要特征。
A scheme for extracting environment features from 1D range data and their interpretation is presented. Segmentation is done by deciding on a measure of model fidelity which is applied to adjacent groups of measurements. The extraction process is considered to include a subsequent matching step where segments which belong to the same landmark ar to be merged while keeping track of those which originate from distinct features. This is done by an agglomerative hierarchical clustering algorithm with a Mahalanobis distance matrix. The method is discussed with straight line segments which are found in a generalized least squares sense using polar coordinates including their first-order covariance estimates. As a consequence, extraction is no longer a real time problem on the level of single range readings, but must be treated on the level of whole scans. Experimental results with three commercially available laser scanners are presented. The implementation on a mobile robot which performs a map-based localization demonstrate the accuracy and applicability of the method under real time conditions. The collection of line segments and associated covariance matrices obtained from the extraction process contains more information about the scene than is required for map-based localization. In a subsequent reasoning step this information is made explicit. By successive abstraction and consequent propagation of uncertainties, a compact scene model is finally obtained in the form of a weighted symbolic description preserving topology information and reflecting the main characteristics of a local observation.