In the 50s, biologists discovered that some electric fish is capable of discriminating the pose as well as the electric and geometric properties of surrounding objects by navigating and measuring the distortions of a self-generated electric field. In this article, we address the challenging issue of ellipsoidal objects pose and size estimation for underwater robots equipped with artificial electric sense. Unlike current methods, the approach can estimate both the position and size in parallel with a single straight trajectory. Neither multipolarization nor reactive self-alignment control are necessary to locate the object. The approach is a purely model-based heuristic that selects the best ellipsoid parameters among a set of potential candidates. It is based on a set of four electric measurements recorded at several positions along the robot trajectory along which the displacement is measured. The efficiency of the method is assessed over numerous experiments with different objects, several positions, and orientations, and two different kinds of water (fresh and salt water). Despite some model simplifications and experimental errors, location and size estimation errors are on average below $\text{1}\,$cm and $\text{15}\%$, respectively, while offering promising perspectives for real-time computation.

中文翻译：

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一种纯粹基于模型的物体姿态和尺寸估计方法

在 50 年代，生物学家发现一些电鱼能够通过导航和测量自生电场的扭曲来区分周围物体的姿势以及电和几何特性。在本文中，我们解决了配备人工电感的水下机器人的椭球体姿态和尺寸估计的挑战性问题。与当前的方法不同，该方法可以与单个直线轨迹平行地估计位置和大小。定位物体既不需要多极化也不需要反应性自对准控制。该方法是一种纯粹基于模型的启发式方法，它在一组潜在候选者中选择最佳椭球参数。它基于在沿机器人轨迹测量位移的多个位置记录的一组四个电测量值。该方法的效率是通过对不同物体、多个位置和方向以及两种不同类型的水（淡水和盐水）进行的大量实验来评估的。尽管存在一些模型简化和实验错误，位置和大小估计误差平均分别低于 $\text{1}\,$cm 和 $\text{15}\%$，同时为实时计算提供了有前景的前景。