Department of Computer Science Engineering, University of Michigan, 2260 Hayward St, Ann Arbor, MI 48109, USA.
Sensors (Basel). 2010;10(11):10356-75. doi: 10.3390/s101110356. Epub 2010 Nov 17.
Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.
特征提取是处理光探测和测距 (LIDAR) 数据的核心步骤。现有的探测器往往利用特定环境的特征：室内（直线）环境的角和线，以及室外环境的树。虽然这些探测器在其预期的环境中表现良好，但它们在不同环境中的性能可能较差。我们描述了一种适用于几乎任何环境的 2D 和 3D LIDAR 数据的通用特征检测器。我们的方法适用于图像处理文献中的经典特征检测方法，特别是多尺度 Kanade-Tomasi 角检测器。该方法能够在不了解环境的情况下，在各种空间尺度上识别高度稳定和可重复的特征，并同时生成有原则的不确定性估计和角描述符。我们在软件模拟和标准数据集上都展示了结果，包括 2D 维多利亚公园和英特尔研究中心数据集，以及 3D MIT DARPA 城市挑战赛数据集。
