Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation

We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.     

背景估计与运动目标检测跟踪

基于视频的自动目标检测和跟踪是计算机视觉中一个重要的研究领域,特别是基于视频的智能车辆监控系统中的运动车辆的检测和跟踪。提出一种自适应的背景估计方法来实时获得当前背景图像,从而分割出运动物体。为了准确地定位运动车辆的区域,采用差分图像投影和边缘投影相结合的方法来定位车体,同时利用双向加权联合图匹配方法对运动车辆区域进行跟踪,即将对运动车辆区域跟踪问题转化为搜索具有最大权的联合图的问题。该算法不仅能实时地定位和跟踪直道上运动的车辆,同时也能实时地定位和跟踪弯道上运动的车辆,从实验结果看,提出的背景更新算法简单,并且运动车辆区域的定位具有很好的鲁棒性,从统计的检测率和运行时间来看,该算法具有很好的检测效果,同时也能满足基于视频的智能交通监控系统的需要。     

基于RTK-DGPS的新型汽车行驶记录仪

当前汽车行驶记录仪大多采用车轮速度传感器,不能真实反映车身速度,本文采用GPS系统对速度进行采集,介绍了该系统的硬件电路组成,并绘制了系统整体框图,设计了系统软件部分.用光电测试验证系统时汽车运动参数进行了对比测试,结果表明该系统能实现动态轨迹的精确测量.     

基于模型参考自适应的速度估计方法

为解决无速度传感器感应电动机矢量控制系统的速度估计问题,以模型参考自适应的理论为基础,利用感应电压矢量进行转速估计并进行仿真,转速估计结果理想,仿真实验所得到的波形与理论分析结果是一致的。并且使相应的无速度传感器矢量控制系统具有良好的静、动态性能。证明了采用模型参考自适应系统,利用感应电压矢量进行转速估计方法的正确性和可行性。     

高速列车在移动闭塞条件下的运行仿真

本文构造了移动闭塞条件下的高速列车计算机运行仿真系统,介绍了系统总体结构,对列车实际运行间隔进行了分析,对各功能模块进行了描述。该系统为进一步分析研究移动闭塞条件下高速列车追踪运行间隔模型、列车的优化控制方法和列车的运行延误影响等提供了仿真平台,对系统实际运用起到了指导的作用。     

汽车黑匣子速度信号采集电路的研究与设计

介绍了一种基于霍尔转速传感器的速度信号采集电路,电路主要为汽车行驶状态记录仪提供转速信号,通过对霍尔转速传感器信号进行放大整形,使处理后的信号转换成标准的方波信号。实际功能验证表明,该转速信号完全能满足汽车行驶状态记录仪的设计需要。     

DSP在测速中的应用

DSP(Digital Signal Processor）俗称数字信号处理器,目前被广泛应用于许多领域,例如数字通信,图像处理,电机控制等方向。本文以TI公司的TMS320LF2407A芯片为例,介绍了DSP在电机测速中的应用,由于TI的2000系列芯片内含捕获单元和正交编码脉冲（QEP）电路,所以可以很方便的用于电机测速和控制。     

A Coordinated Navigation Strategy for Multi-Robots to Capture a Target Moving with Unknown Speed

The paper proposes an algorithm for multi-robot coordination and navigation in order to intercept a target at a long distance. For this purpose, a limit cycle based algorithm using a neural oscillator with phase differences is proposed. The state of target is unknown, under the assumption that it is stationary or in motion with constant unknown speed along a straight line. Using the proposed algorithm, a group of robots is intended to move towards the target in such a way that the robots surround it. While moving to the target, self-collision between the robots is avoided. Moreover, a collision avoidance with static obstacles as well as dynamic target is realized. The robots reach the target at a desired distance, keeping uniformly distributed angles around the target. The algorithm is further extended so that a static interception point for the target can be estimated in place of pursuing a dynamic target, which is referred to as a virtual target in this paper. In other words, the robots move towards the virtual target instead of the actual target. The robots ultimately encircle the actual target when they arrive at the virtual target. The effectiveness of the proposed method is verified through simulation results.     

基于特征点跟踪的运动目标接触时间估计方法研究

解决估计运动目标和静止观测者之间的接触时间(time-to-contact)的问题.首先定义了广义接触时间的概念,并提出了基于特征点跟踪的估计匀速运动目标接触时间的理论依据和利用特征线段估计接触时间的解决思路.随后,提出了一个结合Kalman滤波器的估计匀速运动目标和静止观测者之间接触时间的特征点跟踪方案,并讨论了特征点的选择准则、运动分割的方法、以及所采用的特征点跟踪的方法.最后,针对标定TTC的运动目标序列图像进行接触时间的估计实验,实验的结果是令人满意的.     

高铁环境下快速移动目标的DOA估计

铁路移动通信的制约着高速列车的发展,针对高铁环境下快速运动目标的到达方向(DOA)问题,本文将渐进准循环平稳信号引入高速铁路中存在相对径向加速度的运动场景,研究了一种基于渐近准循环平稳信号的DOA估计算法。该算法充分考虑因加速度产生的相对运动以及多普勒效应在接收端的影响,利用循环平稳性质构造循环自相关矩阵来估计DOA。发现对于加速的目标,接收到的信号会产生渐进循环平稳性。由于噪声不具有循环特征,因此可以在循环频率下提取所需的信号。最后通过数值仿真验证了该方法的有效性。与传统的DOA估计算法相比,该算法具有更好的性能,尤其在径向加速度较大的场景中。     

Learned Models for Estimation of Rigid and Articulated Human Motion from Stationary or Moving Camera

We propose an approach for modeling, measurement and tracking of rigid and articulated motion as viewed from a stationary or moving camera. We first propose an approach for learning temporal-flow models from exemplar image sequences. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are then incorporated to model the movement of regions of rigid or articulated objects. These spatio-temporal flow models are subsequently used as the basis for simultaneous measurement and tracking of brightness motion in image sequences. Then we address the problem of estimating composite independent object and camera image motions. We employ the spatio-temporal flow models learned through observing typical movements of the object from a stationary camera to decompose image motion into independent object and camera motions. The performance of the algorithms is demonstrated on several long image sequences of rigid and articulated bodies in motion.     

基于通信协议的汽车发动机测速系统

在汽车发动机故障诊断和怠速控制中,使用传统发动机测速仪直接测量发动机转速既不方便也不经济.对此,设计了一种以K线为通信接口,以KWP2000为通信协议的发动机测速系统.该系统通过特定的通信协议与发动机电控系统进行通信来获取发动机转速,具有硬件接口电路简单、通信快捷且易于实现等优点.试验结果表明,该新型测速系统具有使用便捷、实时性好和可扩展性强的特点,能满足发动机故障诊断和怠速控制需求.     

合成孔径雷达图像中的动目标速度联合估计

为估计复数合成孔径雷达图像中动目标的速度,本文基于对称散焦滤波技术设计了联合速度估计方法.对于一个动目标雷达图像,该方法首先利用一距离向速度值在距离多普勒平面将目标的距离多普勒轨迹沿距离向对齐,然后利用一方位向速度值构建的对称散焦滤波器沿方位向将该目标图像重新聚焦,得到两个新的目标图像.通过计算这两幅散焦图像的锐度差,可建立一个以距离向速度和方位向速度为变量的锐度差曲面.在曲面的峰值或谷底处即可得到目标速度的估计值.理论分析和实验结果表明了该方法的可行性和有效性.     

Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

This paper presents a multi-cue vision system for the real-time detection and tracking of pedestrians from a moving vehicle. The detection component involves a cascade of modules, each utilizing complementary visual criteria to successively narrow down the image search space, balancing robustness and efficiency considerations. Novel is the tight integration of the consecutive modules: (sparse) stereo-based ROI generation, shape-based detection, texture-based classification and (dense) stereo-based verification. For example, shape-based detection activates a weighted combination of texture-based classifiers, each attuned to a particular body pose. Performance of individual modules and their interaction is analyzed by means of Receiver Operator Characteristics (ROCs). A sequential optimization technique allows the successive combination of individual ROCs, providing optimized system parameter settings in a systematic fashion, avoiding ad-hoc parameter tuning. Application-dependent processing constraints can be incorporated in the optimization procedure. Results from extensive field tests in difficult urban traffic conditions suggest system performance is at the leading edge.     

基于DSP的自适应速度辨识直接转矩控制系统研究

异步电机直接转矩控制能产生快速且良好的鲁棒性响应,采用自适应磁链观测器,取代传统的积分器,构造了新型的速度估计器,并结合模糊控制器,实现对定子磁链准确观测和系统无速度传感器运行状态。基于DSP(TMS320LF2407A)核心芯片建立数字化控制系统。仿真与实验表明,该系统对电机定子磁链的观测精度高,转速估算准确,尤其在低速下能保持很高的性能。     

基于单线圈的速度估计混合模型

实时准确预测出交通速度对于成功的快速路管理系统来说足至关重要的。然而,交通速度并不能直接从单线圈检测器测量出来。该文提出了一种基于单线圈输出量进行速度估计的混合模型方法。该方法分别考虑自由流和拥挤两种不同状态下分别采用不同的模型进行交通速度的估计。模型系数的计算采用已知的单线圈输出量信息和交通流模型进行估计。整个算法采用matlab仿真软件仿真检验,结果表明利用该方法估计出来的速度非常接近于双线圈测量的速度观测值。与以往的算法相比,该方法简单实用并且明显地改善了速度估计。     

基于感应电机状态方程的定子电阻辨识方法的研究

基于感应电机两相同步旋转坐标系状态方程,提出了一种新的定子电阻辨识方案。在分别利用励磁电流微分方程和转矩电流微分方程推算转速的基础上,利用二者的等量关系实现了定子电阻在线辨识。该方案的优点是,定子电阻估算仅需要电机终端可测的定子电压、电流,无须借助转子或定子磁链。仿真实验将其应用于无速度闭环矢量调速系统,实时更新转速估算模块中的定子电阻,论证了该方案对电机参数变化鲁棒性强及对改善转速估算精度的有效性。     

基于观测器的直流伺服电机速度控制

针对直流伺服电机速度控制中经常出现的电机参数变化和外部扰动导致系统性能下降的现象,将外来的扰动转矩和由电机参数变化产生的扰动转矩结合起来,提出了一种等效外来扰动转矩观测器,并基于此观测方法,进而采用电流反馈控制消除观测误差。给出了该速度控制方法的详细推导过程。仿真数据验证了其切实有效性,     

感应电机转子电阻辨识及速度控制策略

感应电机转子电阻的在线辨识和补偿是提高矢量控制系统性能的重要手段。针对感应电机模型的不确定性和非线性,基于扩展卡尔曼滤波(EKF)技术,设计感应电机转子电阻在线估计器,采用模糊控制理论,设计模糊PI速度控制器,根据系统状态的变化,以模糊控制器的输出对PI控制器参数进行修正,从而改善系统的静、动态性能。Matlab仿真结果证明了所设计的EKF转子电阻辨识器和模糊PI控制器的有效性。