From urban terrain models to visible cities

We face the possibility and, in some cases, the results of acquiring accurate digital representations of our cities. The first challenge is to take data from multiple sources, which are often accurate but incomplete, and weave them together into comprehensive models. Because of the size and extent of the data that we can now obtain, this modeling task is daunting and must be accomplished in a semiautomated manner. Once we have comprehensive models, and especially if we can build them rapidly and extend them at will, the next question is what to do with them. Thus, the second challenge is making the models visible. In particular, they must be made interactively visible so users can explore, inspect, and analyze them. We discuss the nature of the acquired data and how we're beginning to meet these challenges and produce visually navigable models. We've developed 3D City, a semiautomated system that supports human intervention at key points to meet the challenge of constructing complete and extended urban models from several data sources. We've already built virtual environments (VEs) for urban planning and emergency response using 3D City.     

框架式地形建模在城市虚拟仿真中的应用

在城市地形建模过程中,由于目前所采用的数据源的高程点密度不能达到高精度地形建模的要求,往往需要内插高程点。提出了基于GIS的“框架式地形建模”方法,在一定条件下不用加密高程点,就可以构建高精度的地形模型。以山东师范大学校本部的地形建模为例,采用基于Multigen Creator/Vega虚拟现实软件平台进行了实证研究,实验结果表明利用该方法可以满足高精度仿真对于地形建模的要求。     

Controlled tracking in urban terrain: Closing the loop

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed‐loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one‐step‐ahead error covariance matrix is transmitted; the received signal goes through a matched‐filter, and curve fitting is used to extract range and range‐rate measurements that feed the LMIPDA‐VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line‐of‐sight.     

适应山头地形的无线传感器网络三维定位机制

使用三维DV-Hop算法对无线传感器进行定位时,存在定位误差较大且不稳定的问题,使三维DV-Hop算法的实际应用能力较差.提出了适应山头地形的无线传感器网络(WSNs)非测距三维定位算法.应用山头地形的特点,将最小二乘法所求得的结果进行投影处理,大幅度提高了定位的精确度.在模拟山头地形的环境中进行仿真实验,相对定位误差为35％左右,比三维DV-Hop算法的相对定位误差有较大幅度的降低,有较高的实用价值.     

山谷地形无线传感器网络DV-Hop定位算法研究

无线传感器网络在森林防火、目标追踪、灾难预警、环境监测等应用中,节点定位是关键技术之一。对山谷地形中无线传感器网络三维DV-Hop定位算法进行了研究。通过对三维DV-Hop定位算法误差分析,找出产生误差的主要原因,提出了相应的改进算法。仿真实验结果证明:改进后的算法提高了节点定位精度,并指出了在山谷地形应用三维DV-Hop定位算法时锚节点比例的参考值。     

基于山区地形的无线传感器网络三维定位机制

针对三维DV-hop无线传感器定位算法在实际地形中定位时误差较大的问题,提出了根据山区地形的网格数据进行较近点投影校正的无线传感器网络非测距三维定位算法。经典的三维DV-hop算法求出未知节点位置后,在该节点附近搜索3个离该节点较近且非共线的山区地形网格上的点,将该节点向上述3个点确定的面进行投影,投影点即为校正后的未知节点位置。山区地形多场景实验表明,该方法较大幅度减小了定位误差。     

The optimal design scheme of an SUGV for surveillance and reconnaissance missions in urban and rough terrain

In this paper, we propose the optimal design scheme for designing an SUGV (Small Unmanned Ground Vehicle) for the purpose of surveillance and reconnaissance in urban and rough terrain, tunnels, sewers, and caves. In general, an SUGV for military operations must be light-weight and small in size to attain portability, and must be able to overcome a variety of obstacles including stairs, curbs, and ramps in urban terrain. Consequently, we propose the optimal design scheme to find the optimal size and optimal actuation system of such an SUGV. The scheme is composed of three parts. The first is to find the optimal body length of the SUGV through estimating the CG point moving path in geometrical coordination when climbing stairs. The second is to find material having the required static friction coefficient by estimating the required traction force and wheel moment. The third is to find the actuation load by estimating the wheel load while it climbs up a ramp with ??. From this scheme, we find an optimally designed SUGV for the purposes of military operations. In this paper, to prove the efficiency of the proposed design scheme, we build an SUGV and experiment its mobility performance. The experimental results demonstrate its stair climbing performance on 20cm high and 40degree inclined ramps. The SUGV weighs 21kg and has a minimum body length of 625mm.     

三维地形场景局部高精度地形快速更新方法

三维地形场景在很多应用中需要通过及时更新局部高精度地形数据来构建重点区域的精细场景,针对局部区域地形数据的动态更新,提出了基于GPU实时网格细分的局部高精度地形的“镶嵌”算法;通过在GPU中对更新区域的地形网格实时插值细分来增加三角面片数,提高地形模型的几何精度,在有效保证更新区域地形绘制精度的同时,并没有增加内存与显存之间的数据传输压力。实验结果表明,更新区域的绘制精度能够满足要求,且执行效率优于现有的常用算法。     

Fuzzy extended Kalman filter for dynamic mobile localization in urban area using wireless network

The problem of accurate mobile positioning in cellular network is very challenging and still subject to intensive research, especially given the uncertainty pervading the signal strength measurements. This paper advocates the use of fuzzy based reasoning in conjunction with Kalman filtering like approach in order to enhance the localization accuracy. The methodology uses TEMS Investigation software to retrieve network information including signal strength and cell-identities of various base transmitter stations (BTS). The distances from the mobile station (MS) to each BTS are therefore generated using Walfish-Ikigami radio propagation model. The performances of the established hybrid estimator −fuzzy extended Kalman filter (FEKF)- are compared with extended Kalman filter approach and fuzzy-control based approach. Both simulation and real-time testing results demonstrate the feasibility and superiority of the FEKF localization based approach.     

Generating extraterrestrial terrain

In recent years, generating both realistic and fanciful terrain has roused substantial interest among computer graphics specialists, motion picture and special-effects artists, geologists, and even mathematicians. Most fractal terrain models have been based on Fourier filtering, midpoint displacement, Poisson faulting, and successive random additions. Techniques such as these, while often creating convincing fractal forgeries of natural mountain terrain, do not always account for the fact that mountains are not statistically self-similar or identical from top to bottom. Nor do many prior methods naturally account for the beautiful ridges, craters, streaks, and various other features often associated with extraterrestrial terrain. The simple, flexible approach I present creates aesthetic extraterrestrial terrain with rich structures of varying asymmetry     

Gravity measurements and terrain corrections using a digital terrain model in the NW Himalaya

Areas recently gravity surveyed in the NW Himalaya are characterized by high-elevation and high-amplitude topographic undulations. A new method of applying combined Bouguer and terrain corrections using a digital terrain model is highly accurate and offers advantages over conventional techniques by saving efforts and being more flexible. Partitioning parameters for station-dependent inner-zone compartments and station-independent outer zones can be optimally selected for the desired accuracy requirements. A digital terrain database is used to obtain the outer-zone corrections. In the situation of the NW Himalaya surveys, a 1.2 km inner zone is divided into 112 compartments for each station and a digital terrain database containing nearly 16 000 data points for 30″×30″ compartments was applied using the computer program EFFECT.FOR, to compute combined Bouguer and terrain corrections for a 20 km range. The terrain corrections between 20 and 170 km were computed using National Geophysical Data Centre (NGDC) 5′×5′ gridded global elevation database. The magnitude of the terrain correction varies between 3 and 50 mGal. The effects of the 20 km range terrain correction are more pronounced on short-medium wavelength anomalies. The Swarghat gravity high is further enhanced while several high-frequency pseudo-anomalies disappear after applying the terrain corrections. The refined Bouguer anomaly varies from −160 mGal at the southern end of the section, to −310 mGal at the northern end, suggesting a Moho depth variation from 45 to nearly 60 km. The steepness of the northward negative gravity gradient, typical for the Himalaya, is considerably reduced after applying a terrain correction for the 170 km range.     

Robot navigation in an unexplored terrain

Navigation planning is one of the most vital aspects of an autonomous mobile robot. Robot navigation for completely known terrain has been solved in many cases. Comparatively less research dealing with robot navigation in unexplored obstacle terrain has been reported in the literature. In recent times this problem has been addressed by adding learning capability to a robot. The robot explores terrain using sensors as it navigates, and builds a terrain model in an incremental manner. In this article we present concurrent algorithms for robot navigation in unexplored terrain. The performance of the concurrent algorithms is analyzed in terms of planning time, travel time, scanning time, and update time. The analysis reveals the need for an efficient data structure to store an obstacle terrain model in order to reduce traversal time, and also to incorporate learning. A modified adjacency list is proposed as a data structure for storing a spatial graph that represents an obstacle terrain. The time complexities of the algorithms that access, maintain, and update the spatial graph are estimated, and the effectiveness of the implementation is illustrated.     

Probabilistic terrain classification in unstructured environments

Autonomous navigation in unstructured environments is a complex task and an active area of research in mobile robotics. Unlike urban areas with lanes, road signs, and maps, the environment around our robot is unknown and unstructured. Such an environment requires careful examination as it is random, continuous, and the number of perceptions and possible actions are infinite.We describe a terrain classification approach for our autonomous robot based on Markov Random Fields (MRFs ) on fused 3D laser and camera image data. Our primary data structure is a 2D grid whose cells carry information extracted from sensor readings. All cells within the grid are classified and their surface is analyzed in regard to negotiability for wheeled robots.Knowledge of our robot’s egomotion allows fusion of previous classification results with current sensor data in order to fill data gaps and regions outside the visibility of the sensors. We estimate egomotion by integrating information of an IMU, GPS measurements, and wheel odometry in an extended Kalman filter.In our experiments we achieve a recall ratio of about 90% for detecting streets and obstacles. We show that our approach is fast enough to be used on autonomous mobile robots in real time.     

基于建筑特征及二维地图的复杂城市场景中移动机器人视觉定位算法

针对城市环境中全球定位系统(GPS)信号易受到高层建筑遮挡而无法提供准确位置信息的问题,提出了一种基于建筑物竖直侧平面特征及建筑物二维轮廓地图的移动机器人定位方法。该方法利用车载视觉,首先对两视图间的竖直直线特征进行匹配;然后基于匹配的竖直线特征对建筑物的竖直侧平面进行重建;最后,利用建筑物竖直侧平面特征及建筑物二维俯视轮廓地图,设计了一种基于随机采样一致性(RANSAC)的移动机器人视觉定位算法,从而解决了在建筑物方向任意的复杂城市环境中的机器人定位问题。实验结果表明,算法的平均定位误差约为3.6m,可以有效地提高移动机器人在复杂城市环境中自主定位的精度及鲁棒性。     

Frequency-based underwater terrain segmentation

A method for segmenting three-dimensional data of underwater unstructured terrains is presented. The three-dimensional point clouds are converted to two-dimensional elevation maps and analyzed for segmentation in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground. The cut-off frequency, below which the frequency components form the ground surface, is determined automatically using peak detection. The user can also specify a maximum admissible size of objects to drive the automatic detection of the cut-off frequency. The points above the estimated ground surface are clustered via standard proximity clustering to form object segments. The precision of the segmentation is compared against ground truth hand labelled data acquired by a stereo camera pair and a structured light sensor. It is also evaluated for registration error when the extracted segments are used for sub-map alignment. The proposed approach is compared to three point cloud based and two image based segmentation algorithms. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.     

Fast replanning for navigation in unknown terrain

Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A/sup */ (D/sup */) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D/sup */ that determines the same paths and thus moves the robot in the same way but is algorithmically different. D/sup */ Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D/sup */. We believe that our results will make D/sup */-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.     

Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self‐supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. © 2012 Wiley Periodicals, Inc.     

Flying over a polyhedral terrain

We consider the problem of computing shortest paths in three-dimensions in the presence of a single-obstacle polyhedral terrain, and present a new algorithm that for any p?1, computes a (c+ε)-approximation to the Lp-shortest path above a polyhedral terrain in time and O(nlogn) space, where n is the number of vertices of the terrain, and c=²(p−1)/p. This leads to a FPTAS for the problem in L₁ metric, a -factor approximation algorithm in Euclidean space, and a 2-approximation algorithm in the general Lp metric.     

不同视点海底图像拼接算法

对于深海水下摄影,由于相机平移和旋转,图像对应关系很难自动生成,因此需对应点映射来求解图像间的变换.本文通过摄像机自身所带仪器,测量摄像机焦点和任意物点所在位置,得出不同图像中相同物点所对应的像点.对于两幅图像的映射关系,原理上讲,一般输入三组对应点即可,但考虑到误差和相关性等因素,输入较多的对应点,时所求结果进行优化.将一幅图像映射到另一幅图像所在平面,对两幅图像进行平滑拼接.最后给出计算机实验结果.