Autonomous Salient Feature Detection through Salient Cues in an HSV Color Space for Visual Indoor Simultaneous Localization and Mapping

For successful simultaneous localization and mapping (SLAM), perception of the environment is important. This paper proposes a scheme to autonomously detect visual features that can be used as natural landmarks for indoor SLAM. First, features are roughly selected from the camera image through entropy maps that measure the level of randomness of pixel information. Then, the saliency of each pixel is computed by measuring the level of similarity between the selected features and the given image. In the saliency map, it is possible to distinguish the salient features from the background. The robot estimates its pose by using the detected features and builds a grid map of the unknown environment by using a range sensor. The feature positions are stored in the grid map. Experimental results show that the feature detection method proposed in this paper can autonomously detect features in unknown environments reasonably well.     

General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data

This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments.     

Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots

Abstract

Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.     

基于中心距离特征的联机手写维吾尔文全形态字母识别研究

为了提高联机手写维吾尔文字母的正确识别率,根据维吾尔文字母的手写特点,提出了中心距离特征CDF（Center Distance Feature）、并基于CDF进行了一系列识别实验。在实验中,该文采集了400个人的手写字母样本,利用CDF的三种不同的实施方案（CDF-2,CDF-4,CDF-8）分别对维吾尔文字的32个母独立形态和128个所有形态进行了识别实验,并对实验结果进行了分析。实验结果表明,CDF是一种非常适合于维吾尔文字母识别的特征,有待于进一步改进和优化。     

面向胎盘植入产前诊断的医学语义特征提取算法

胎盘植入由于其临床特征隐匿,尚无一种敏感性、特异性高的产前诊断手段,因此文中将数据的特征提取方法引入胎盘植入产前诊断领域,从特征相关性的角度,提出胎盘植入有效医学语义的多目标特征优化问题,并给出求解该问题的一种改进的非支配排序遗传算法II( NSGA-II)。基于实际胎盘植入相关临床数据的计算结果表明,文中算法能从复杂的胎盘植入相关临床数据中提取具有胎盘植入有效语义的特征集合。经过接收者操作特征( ROC)曲线分析,提取的特征医学语义具有较高的诊断价值,可为产科医师研究胎盘植入的发病机制和及时产前诊断提供有效的辅助手段。文中研究还发现,一些临床生化检查指标具有重要作用,可作为胎盘植入产前诊断的有效依据。