RI: Small: Minimalistic Estimators for Navigation of Miniature Mobile Platforms

提出了一类新的估计方法，可以最大限度地利用最小资源进行高精度的位姿跟踪。该项目开发了第一个正式的方法，用于为小型便携式设备提供参数选择(例如，相机帧速率、图像分辨率、检测到的特征的数量和类型)，以延迟电池的耗尽。该方法基于对视觉和惯性传感器姿态跟踪问题特性的严格研究，(1)确定了可达到的估计精度的基本极限，(2)允许作为传感和处理资源的使用的函数的精度的分析预测。这使得优化分配系统资源的算法的开发成为可能，该算法的设计依赖于优化框架，其中估计误差构成要最小化的成本函数，资源限制被显式地建模为约束，并且所有相关的设计参数(例如，相机帧速率、所使用的特征的数量)都是优化变量。这项研究工作的直接影响是提高了不同应用中导航任务的成本效益和准确性。开发的技术可通过用于移动电话设备的开源位置跟踪软件提供给更广泛的社区，并提供有用的辅助技术。我们邀请K-12年级的学生参加当地的外展活动，并向他们介绍工程学，并与加州大学河滨分校的MESA合作，MESA是加州大学河滨分校的一个长期项目，在吸引代表人数不足的少数族裔学生进入科学和工程领域方面有过证明的记录。

A new class of estimation methods are developed that can optimally utilize minima resources for high-precision pose tracking. This project develops the first formal methodologies for providing selections of parameters (e.g., camera frame rate, image resolution, number and type of detected features) for small portable devices to delay depletion of the battery. The approach is based on a rigorous study of the properties of the pose tracking problem with visual and inertial sensors and (1) identifies the fundamental limits of the attainable estimation accuracy, and (2) allows the analytical prediction of the accuracy as a function of the use of the sensing and processing resources. This makes possible the development of algorithms that optimally allocate system resources whose design relies on an optimization framework where the estimation errors constitute the cost function to be minimized, the resource limitations are explicitly modeled as constraints, and all the relevant design parameters (e.g., camera frame rate, number of features used) are the optimization variables. The immediate impact of this research effort is the increased cost efficiency and accuracy for navigation tasks in diverse applications.The developed technology is available to the wider community through open-source position-tracking software for mobile phone devices and provides useful assistive technology. We engage K-12 students at local outreach events and introduce them to engineering as well collaborate with MESA, a long-standing program at the University of California at Riverside with a proven record of attracting underrepresented minority students to science and engineering.