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Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera

机译:使用3D ToF相机进行神经进化的移动机器人自我运动估计

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An innovative neuro-evolutionary approach for mobile robot egomotion estimation with a 3D ToF camera is proposed. The system is composed of two main modules following a preprocessing step. The first module is a Neural Gas network that computes a Vector Quantization of the preprocessed camera 3D point cloud. The second module is an Evolution Strategy that estimates the robot motion parameters by performing a registration process, searching on the space of linear transformations, restricted to the translation and rotation, between the codebooks obtained for successive camera readings. The fitness function is the matching error between the predicted and the observed codebook corresponding to the next camera readings. In this paper, we report results of an implementation of this system tested on data from a real mobile robot, and provide several comparisons between our and other well-known registration algorithms.
机译:提出了一种创新的神经进化方法,用于利用3D ToF相机进行移动机器人自我估计。该系统由两个主要模块组成,并经过一个预处理步骤。第一个模块是神经气体网络,用于计算预处理的相机3D点云的矢量量化。第二个模块是“进化策略”,它通过执行配准过程,在为连续摄像头读取而获得的码本之间搜索受限于平移和旋转的线性变换空间上,来估计机器人的运动参数。适应度函数是与下一个摄像机读数相对应的预测码本和观察码本之间的匹配误差。在本文中,我们报告了该系统的实现结果,该系统在真实的移动机器人的数据上进行了测试,并提供了我们与其他著名的注册算法之间的几种比较。

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