嫦娥五号探月雷达的数据处理方法研究

探月雷达对于认识月球和开发月球资源具有重要意义,我国发射的嫦娥五号探测器已于2020年12月1日在月球表面着陆,圆满完成了嫦娥三期工程"采样返回"任务.根据嫦娥五号的任务安排,月壤结构仪在月表采集的雷达数据传回地表后,需要准时对月壤钻头下方2m深度范围内的月壤结构和可能存在的月岩进行高分辨率成像,为后续月壤钻取任务提供...     

BGC-100抱罐车研制

针对国内现代化钢厂规模化生产对研制大吨位抱罐车提出的要求,研制了BGC-100式抱罐车.介绍了BGC-100式抱罐车的结构组成,技术特征及创新点.     

铰接式车辆中双万向联轴节运动分析

从运动学的观点分析了铰接式车辆前传动轴和双万向节的布置原则，并进而分析了因制造和安装误差造成的传动不均匀性。     

Human Motion Planning Based on Recursive Dynamics and Optimal Control Techniques

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling themotion of articulated figures. A quasi-Newton nonlinear programmingtechnique (super-linear convergence) is implemented to solve minimumtorque-based human motion-planning problems. The explicit analyticalgradients needed in the dynamics are derived using a matrix exponentialformulation and Lie algebra. Cubic spline functions are used to make thesearch space for an optimal solution finite. Based on our formulations,our method is well conditioned and robust, in addition to beingcomputationally efficient. To better illustrate the efficiency of ourmethod, we present results of natural looking and physically correcthuman motions for a variety of human motion tasks involving open andclosed loop kinematic chains.     

Image Manifolds which are Isometric to Euclidean Space

Recently, the Isomap procedure [10] was proposed as a new way to recover a low-dimensional parametrization of data lying on a low-dimensional submanifold in high-dimensional space. The method assumes that the submanifold, viewed as a Riemannian submanifold of the ambient high-dimensional space, is isometric to a convex subset of Euclidean space. This naturally raises the question: what datasets can reasonably be modeled by this condition? In this paper, we consider a special kind of image data: families of images generated by articulation of one or several objects in a scene—for example, images of a black disk on a white background with center placed at a range of locations. The collection of all images in such an articulation family, as the parameters of the articulation vary, makes up an articulation manifold, a submanifold of L ². We study the properties of such articulation manifolds, in particular, their lack of differentiability when the images have edges. Under these conditions, we show that there exists a natural renormalization of geodesic distance which yields a well-defined metric. We exhibit a list of articulation models where the corresponding manifold equipped with this new metric is indeed isometric to a convex subset of Euclidean space. Examples include translations of a symmetric object, rotations of a closed set, articulations of a horizon, and expressions of a cartoon face. The theoretical predictions from our study are borne out by empirical experiments with published Isomap code. We also note that in the case where several components of the image articulate independently, isometry may fail; for example, with several disks in an image avoiding contact, the underlying Riemannian manifold is locally isometric to an open, connected, but not convex subset of Euclidean space. Such a situation matches the assumptions of our recently-proposed Hessian Eigenmaps procedure, but not the original Isomap procedure.     

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation; the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body. Most of this work was done while the first author was with Boston University.     

Design and Use of Linear Models for Image Motion Analysis

Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomena; to estimate the coefficients of these linear models; and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic motion features such as motion discontinuities and moving bars; and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.     

Learning the Statistics of People in Images and Video

This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a background distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.     

基于恒星敏感器的月球车航向角确定算法

针对月球特殊的工作环境，阐述了现有技术条件下，我国自主式月球探测车采用惯性导航系统进行位姿确定时，航向角参数误差随着时间不断累积的缺点，进而提出了一种基于星历表数据和恒星敏感器相结合的月球车航向角精确值的确定算法，定期对惯性导航系统中的航向角参数进行修正，以提高月球车位置和姿态精度。重点介绍了航向角精确值的确定步骤，并对由航向角误差引起的定位误差进行了仿真分析，仿真结果证明了航向角修正的必要性和此方法的可行性。     

基于月貌匹配的视觉导航方法

针对月面探测器在接近、下降、着陆阶段所要完成的精确导航任务,提出了一种基于月貌匹配的月面探测器的视觉导航方法.月面探测器下降过程中,与探测器固联的CCD相机所拍摄的月面图像相对于图像数据库存在较大的旋转和缩放.采用Scale Invariant Feature Transform(SIFT)算法提取月貌特征并在图像数据库中进行匹配,克服了存在较大的旋转和缩放的图像的匹配问题.最后,利用月貌匹配点的地理信息,通过2D/3D位姿估计方法,估计出探测器在地理坐标系下绝对的位置和姿态.仿真结果表明该方法有效,可以应用在月面探测器在接近、下降、着陆段的导航任务中.