Estimating forest canopy fuel parameters using LIDAR data

Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R²=0.86; ln(crown bulk density): R²=0.84; canopy base height: R²=0.77; canopy height: R²=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.     

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM??s arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot??s dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.     

Autonomous navigation and obstacle avoidance using navigation laws with time-varying deviation functions

In this paper we introduce a new family of navigation functions for robot navigation and obstacle avoidance. The method can be used for both path finding and real-time path planning. Each navigation function is composed of three parts: a proportionality term, a deviation function and a deviation constant. Deviation functions are time-varying functions satisfying certain conditions. These functions and parameters are updated in real-time to avoid collision with obstacles. Our strategy uses polar kinematics equations to model the navigation problem in terms of the range and direction between the robot and the goal. The obstacles are mapped to polar planes, and represented by the range and the direction from the robot or the final goal in polar coordinates. This representation gives a certain weight to the obstacles based on their relative position from the robot and facilitates the design of the navigation law. There exists an infinite number of navigation functions obtained by changing the proportionality constant, the deviation constant or the deviation function. This offers an infinite number of possibilities for the robot's path. Our navigation strategy is illustrated using an extensive simulation where different navigation parameters are used.     

Autonomous mobile robots navigation using RBF neural compensator

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.     

多传感器数据融合的智能电动车自主导航

以四轮驱动电动车为研究对象,将智能电动车上多种传感器所采集的信息进行处理与融合,实现校园环境里的自主行驶与导航.提出了在空旷路段和沿墙导航的控制决策,着重研究了沿墙算法和入弯算法,使智能电动车在不同的环境下可以有效地完成自主导航.实验结果表明:该导航控制策略和算法具有较高的可靠性.     

Autonomous robot navigation using optimal control of probabilistic regular languages

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment.     

Autonomous navigation for high altitude satellites

The determination of the orbit of high altitude satellites with an accurate horizontal charge coupled device (CCD) sensor is considered using the extended Kalman filter. The measurement nonlinearity is removed by using a coordinate transform, and the corresponding steady state error is less than the steady state error in the Cartesian coordinate system. The performance of both of the navigational filters is evaluated for a reference geosynchronous orbit as a function of measurement error. The reduction of measurement uncertainty decreased steady state errors in position and velocity.     

Autonomous navigation system of near-Earth spacecraft

An autonomous navigation system for near-Earth spacecraft is described; this system allows determination of the satellite orbit and prediction of its motion parameters. Radio navigation measurements of GLONASS and GPS satellite systems are used for this purpose. The autonomous navigation system is designated for operation on near-Earth orbits which do not go beyond the navigation areas of GLONASS and/or GPS and on orbits with large eccentricity whose apocenter is at a distance of 50–70 thousand km from the Earth’s surface. The developed methods and algorithms for orbit determination are based on the application of laws of motion dynamics of a spacecraft directly at processing primary phase measurements of the carrier frequency and code pseudo-range using an extended measurement base. Algorithms for determination of motion parameters of the spacecraft and results of simulation and operation of a model system are presented. The possibility of creation of an onboard autonomous navigation system with precision and reliability higher than those of the ground measuring complex is demonstrated.     

Autonomous mobile robot navigation and learning

Research focused on the development and experimental validation of intelligent control techniques for autonomous mobile robots able to plan and perform a variety of assigned tasks in unstructured environments is presented. In particular, an autonomous mobile robot, HERMIES-IIB intelligence experiment series, is described. It is a self-powered, wheel-driven platform containing an onboard 16-node Ncube hypercube parallel processor interfaced to effectors and sensors through a VME-based system containing a Motorola 68020 processor, a phased sonar array, dual manipulator arms, and multiple cameras. Research on navigation and learning is examined     

Deep reinforcement learning for drone navigation using sensor data

Neural Computing and Applications - Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and...     

Autonomous robot navigation using Retinex algorithm for multiscale image adaptability in low-light environment

Intelligent Service Robotics - This paper proposes an improved Retinex theory based on a weighted guided filter method to enhance images in low-light conditions. The captured images under low...     

Efficient method for lossless LIDAR data compression

Light Detection and Ranging (LIDAR) has become one of the prime technologies for rapid collection of vast spatial data, usually stored in a LAS file format (LIDAR data exchange format standard). In this article, a new method for lossless LIDAR LAS file compression is presented. The method applies three consequent steps: a predictive coding, a variable-length coding and an arithmetic coding. The key to the method is the prediction schema, where four different predictors are used: three predictors for x, y and z coordinates and a predictor for scalar values, associated with each LIDAR point. The method has been compared with the popular general-purpose methods and with a method developed specially for compressing LAS files. The proposed method turns out to be the most efficient in all test cases. On average, the LAS file is losslessly compressed to 12% of its original size.     

Autonomous navigation during the final ascent of a spacecraft into the geostationary orbit. Autonomous integrated navigation system concept

Journal of Computer and Systems Sciences International - This paper is the first in a series of works devoted to the problems of a spacecraft in the geostationary orbit autonomous operation. The...     

基于四叉树划分的地面激光雷达数据简化

在分析地面激光雷达的数据特性和现有数据简化方法的基础上,提出了一种基于四叉树划分的地面激光雷达数据简化方案,给出了算法的基本思想和实现方法。实验表明该算法对于呈平面分布的点云数据简化能达到很高的压缩比,并具有良好的边缘保持性能。     

Autonomous robot navigation in outdoor cluttered pedestrian walkways

This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through nonmodified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors, which include wheel encoders, an inertial measurement unit, a differential global positioning system, and four laser scanner sensors. All the computation was done on a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system's perception, road extraction, and motion planning are detailed. The system was used and tested in a 1‐km autonomous robot navigation challenge held in the City of Tsukuba, Japan, named “Tsukuba Challenge 2007.” The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first on campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation. © 2009 Wiley Periodicals, Inc.     

Urban scene understanding from aerial and ground LIDAR data

We present a framework to segment cultural and natural features, given 3D aerial scans of a large urban area, and (optionally) registered ground level scans of the same area. This system provides a primary step to achieve the ultimate goal of detecting every object from a large number of varied categories, from antenna to power plants. Our framework first identifies local patches of the ground surface and roofs of buildings. This is accomplished by tensor voting that infers surface orientation from neighboring regions as well as local 3D points. We then group adjacent planar surfaces with consistent pose to find surface segments and classify them as either the terrain or roofs of buildings. The same approach is also applied to delineate vertical faces of buildings, as well as free-standing vertical structures such as fences. The inferred large structures are then used as geometric context to segment linear structures, such as power lines, and structures attached to walls and roofs from remaining unclassified 3D points in the scene. We demonstrate our system on real LIDAR datasets acquired from typical urban regions with areas of a few square kilometers each, and provide a quantitative analysis of performance using externally provided ground truth.     

结合体元数据结构的机载LIDAR建筑物检测

目的 目前,点云、栅格格网及不规则三角网等建筑物检测中常用的离散机载激光雷达（LIDAR）点云数据表达方式存在模型表达复杂、算法开发困难、结果表达不准确及难以表达多返回数据等缺点。为此,针对LIDAR点云体元结构模型构建及在此基础上的建筑物检测展开研究,提出一种基于体元的建筑物检测算法。方法 首先将点云数据规则化为二值（即1、0值,分别表示体元中是否包含有激光点）3D体元结构。然后利用3D滤波算法将上述体元结构中表征数据点的体元分类为地面和非地面体元。最后,依据建筑物边缘的接近直线、跳变特性从非地面体元中搜寻建筑物边缘作为种子体元进而标记与其3D连通的非地面体元集合为建筑物体元。结果 实验基于ISPRS（international society for photogrammetry and remote sensing）提供的包含了不同的建筑物类型的城区LIDAR点云数据测试了"邻域尺度"参数的敏感性及提出算法的精度。定量评价的结果表明：56邻域为最佳邻域尺度;建筑物的检测质量可达到95%以上——平均完整度可达到95.61%、平均正确率可达95.97%。定性评价的结果表明：对大型、密集、不规则形状、高低混合及其他屋顶类型比较特殊的复杂建筑物均可成功检测。结论 本文提出的建筑物检测算法采用基于体元空间邻域关系的搜索标记方式,可有效实现对各类建筑目标特别是城市建筑目标的检测,检测结果易于建模3D建筑物模型。     

基于天文/GPS的HEO卫星自主导航方法

为了实现大椭圆轨道(HEO)卫星高精度自主导航,提出一种将直接敏感地平天文导航与全球定位系统(GPS)相结合的组合导航方法.首先,分析卫星轨道??2运动模型及其所受空间摄动,建立卫星轨道动力学模型;然后,分析单一使用天文导航和GPS的优缺点,根据HEO卫星对GPS的可见性,提出在远地点只采用天文导航,而在近地点采用以天文导航为主、适时引入GPS信号进行位速测量辅助修正的方法.通过计算机仿真和结果分析表明了所提出的设计方法导航精度比单一天文导航提高72.4%～85.6%.     

Wheeled mobile robot navigation using proportional navigation

We present a method for wheeled mobile robot navigation based on the proportional navigation law. This method integrates the robot's kinematics equations and geometric rules. According to the control strategy, the robot's angular velocity is proportional to the rate of turn of the angle of the line of sight that joins the robot and the goal. We derive a relative kinematics system which models the navigation problem of the robot in polar coordinates. The kinematics model captures the robot path as a function of the control law parameters. It turns out that different paths are obtained for different control parameters. Since the control parameters are real, the number of possible paths is infinite. Results concerning the navigation using our control law are rigorously proven. An extensive simulation confirms our theoretical results.