Stochastic performance, modeling and evaluation of obstacle detectability with imaging range sensors
Stochastic performance, modeling and evaluation of obstacle detectability with imaging range sensors
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成像距离传感器障碍物检测的随机性能、建模和评估
DOI：
10.1109/70.338533
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
P. Grandjean
中科院分区：
文献类型：
--
作者：
L. Matthies;P. Grandjean
无人地面车辆（UGV）障碍物检测系统性能的统计建模和评估对于传感器系统的设计、评估和比较至关重要。在这份报告中，我们解决这个问题的成像范围传感器的评估问题分为两个层次：质量的范围数据本身和质量的障碍物检测算法应用到范围数据。我们回顾现有的模型的质量范围内的数据从立体视觉和AM-CW激光雷达，然后使用这些导出一个新的模型的质量的一个简单的障碍物检测算法。该模型预测检测到障碍物的概率和误报的概率，作为障碍物的大小和距离，传感器的分辨率和范围数据中的噪声水平的函数。我们评估这些模型的实验使用的范围数据从立体图像对砾石路与已知的障碍物在几个距离。结果表明，该方法是一个很有前途的工具，预测和评估的障碍物检测与成像距离的性能。>
Statistical modeling and evaluation of the performance of obstacle detection systems for unmanned ground vehicles (UGV's) is essential for the design, evaluation and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range. >