RI-Small: Adaptive Parameter and State Estimation on Groups with Application to Sensor-Based Control

本研究旨在开发自适应方法，使基于传感器的系统能够从自己的感官经验中学习，以提高其感知精度和灵活性。我们将研究基于传感器的参数估计和组上的状态估计问题，特别关注刚体变换组。这些组结构问题自然出现在三维传感（例如激光扫描仪和多波束测深声纳等距离扫描仪、多普勒声纳和基于视觉的状态估计）和动态估计（例如基于图像的状态估计、基于视觉的控制和多自由度车辆导航和控制）中。针对未知参数集或状态具有群结构的常见问题，目前的识别技术很少。这与用于参数和状态估计的各种已知技术（即最小二乘和自适应）形成对比，在这种情况下，未知参数或状态在植物方程中线性出现，并且未知是线性向量空间的一个元素。预期的影响将是机器人系统能够自适应学习，以提高其导航和传感精度。我们将把这些方法应用于水下航行器传感和控制中出现的实际问题。

This research seeks to develop adaptive methods that will enable sensor-based systems to learn from their own sensory experience in order to improve their perceptual precision and dexterity. We will investigate the problem of sensor-based parameter estimation and state estimation on groups, with particular focus on the group of rigid body transformations. These group-structured problems naturally arise in three-dimensional sensing (e.g. range scanners such as laser scanners and multi-beam bathymetric sonars; Doppler sonar; and vision based state estimation) and dynamic state estimation (e.g. image-based state estimation; vision based control; and multi-degree-of-freedom vehicle navigation and control). Few identification techniques presently exist for the common problem in which the unknown parameter set or state possesses group structure. This is in contrast to the variety of well-known techniques (i.e. least-squares and adaptive) for parameter and state estimation for the case in which the unknown parameter or state appears linearly in the plant equations and the unknown is an element of a linear vector space. The anticipated impact will be robotic systems capable of adaptively learning to improve their navigation and sensing accuracy. We will apply these approaches to actual real-world problems arising in underwater vehicle sensing and control.