Probabilistic inverse sensor model based Digital Elevation Map creation for an omnidirectional stereovision system

Szilard Mandici, S. Nedevschi
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引用次数: 2

Abstract

The objective of the paper is to present an original solution for building a high accuracy Digital Elevation Map (DEM), from down-looking omnidirectional stereo system data, used for surrounding perception. For this reason, an accurate probabilistic inverse sensor model of the omnidirectional stereo sensor is estimated based on training data. The obtained model considers not only the Gaussian spread of 3D points but also the systematic translations and errors from the calibration and rectification processes. The inverse sensor model is obtained by calculating the prior probabilities of 3D points corresponding to each DEM cell and the direct sensor model, describing the way measurements are acquired. The direct sensor model is calculated using an umbrella based modified Shepard trilinear interpolation of individual measurements in space. The results of the interpolation (σx, σy, σz, μx, μy, μz,) are stored in a 3D lookup table which performs a discretization of 3D space into cuboids. For each 3D point the probability of correspondence to the neighboring cells is calculated and the obtained values are added to the height histogram of each cell. Instead of adding to a single bucket in the histogram, the contribution is spread based on the standard deviation of the height. In order to increase the contribution of individual points in sparse areas and to decrease it in dense areas, the relative density of 3D points in local patches is precomputed and is used as a decreasing exponential term. Based on the obtained models, an improved DEM creation algorithm is applied. The obtained elevation map provides better results both in terms of accuracy and detection rate.
基于概率逆传感器模型的全向立体视觉系统数字高程图生成
本文的目的是提出一个原创的解决方案,建立一个高精度的数字高程地图(DEM),从向下看全向立体系统数据,用于周围感知。为此,基于训练数据估计出全向立体传感器的精确概率逆传感器模型。该模型不仅考虑了三维点的高斯分布,而且考虑了标定和校正过程的系统平移和误差。通过计算每个DEM单元对应的三维点和直接传感器模型的先验概率,获得逆传感器模型,描述测量的获取方式。直接传感器模型的计算采用基于伞的改进Shepard三线性插值的空间测量。插值结果(σx, σy, σz, μx, μy, μz,)存储在三维查找表中,该查找表将三维空间离散为长方体。对于每个3D点,计算其与相邻单元对应的概率,并将得到的值添加到每个单元的高度直方图中。不是添加到直方图中的单个桶中,而是根据高度的标准偏差散布贡献。为了在稀疏区域增加单个点的贡献,在密集区域减少单个点的贡献,预先计算局部斑块中三维点的相对密度,并将其作为指数递减项。在此基础上,提出了一种改进的DEM创建算法。得到的高程图在精度和检出率方面都有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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