基于相对特征的越野地形可通行性分析

自主地面车在越野环境下导航面临最困难的问题之一是对地形的理解，分析感知到的越野地形，作适合于自主车导航的可通行性分析。本文提出了越野高程地形的相对不变性概念，并利用这种性质提取出在一定尺度范围内地形具有的相对不变特征，如地形坡度、起伏度和粗糙度，最后基于模糊规则组合各特征对地形的可通行性进行评估。自主车越野导航实验表明，本文算法稳定有效，能满足自主地面车越野导航的需要。     

Navigating a mobile robot by a traversability field histogram.

This paper presents an autonomous terrain navigation system for a mobile robot. The system employs a two-dimensional laser range finder (LRF) for terrain mapping. A so-called "traversability field histogram" (TFH) method is proposed to guide the robot. The TFH method first transforms a local terrain map surrounding the robot's momentary position into a traversability map by extracting the slope and roughness of a terrain patch through least-squares plane fitting. It then computes a so-called "polar traversability index" (PTI) that represents the overall difficulty of traveling along the corresponding direction. The PTIs are represented in a form of histogram. Based on this histogram, the velocity and steering commands of the robot are determined. The concept of a virtual valley and an exit condition are proposed and used to direct the robot such that it can reach the target with a finite-length path. The algorithm is verified by simulation and experimental results.     

基于地形预测与修正的搜救机器人可通过度

针对搜救机器人的自主导航问题,提出了一种新的可通过度计算算法．文中定义的可通过度包含预测 可通过度和实时可通过度两个部分．通过图像处理的方法计算前方地面的粗糙度和起伏度从而得到预测可通过度, 机器人向可通过度最大的区域运动,在运动过程中通过测量车体的姿态变化率和履带与地面的打滑度来修正预测可 通过度对机器人的导引带来的偏差,并通过旋转式超声测距数据获取距离信息,去除障碍物对纹理信息的干扰．野 外条件下的实际运行实验证明了该导航算法的有效性．     

Vision‐based terrain characterization and traversability assessment

This article presents novel techniques for real‐time terrain characterization and assessment of terrain traversability for a field mobile robot using a vision system and artificial neural networks. The key terrain traversability characteristics are identified as roughness, slope, discontinuity, and hardness. These characteristics are extracted from imagery data obtained from cameras mounted on the robot and are represented in a fuzzy logic framework using perceptual, linguistic fuzzy sets. The approach adopted is highly robust and tolerant to imprecision and uncertainty inherent in sensing and perception of natural environments. The four traversability characteristics are combined to form a single Fuzzy Traversability Index, which quantifies the ease‐of‐traversal of the terrain by the mobile robot. Experimental results are presented to demonstrate the capability of the proposed approach for classification of different terrain segments based on their traversability. © 2001 John Wiley & Sons, Inc.     

Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three‐dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT‐TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT‐OM) maps. In addition to geometrical information, we propose to augment the NDT‐OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support‐vector machine classifier to discriminate between traversable and nondrivable areas of the NDT‐TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.     

Traversability analysis for autonomous driving in complex environment: A LiDAR-based terrain modeling approach

For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial-temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real-time and outperforms state-of-the-art approaches.     

New Traversability Indices and Traversability Grid for Integrated Sensor/Map‐Based Navigation

This paper presents new measures of terrain traversability at short range and long range of a mobile robot; namely, local and global traversability indices. The sensor‐based local traversability index is related by a set of linguistic rules to large obstacles and surface softness within a short range of the robot measured by on‐board sensors. The map‐based global traversability index is obtained from the terrain topographic map, and is based on major surface features such as hills and lakes within a long range of the robot. These traversability indices complement the mid‐range sensor‐based regional traversability index introduced earlier. Each traversability index is represented by four fuzzy sets with the linguistic labels {POOR, LOW, MODERATE, HIGH}, corresponding to surfaces that are unsafe, moderately‐unsafe, moderately‐safe, and safe for traversal, respectively. The global terrain analysis also leads to the new concepts of traversability map and traversability grid for representation of terrain quality based on the global map information. The traversability indices are used in two sensor‐based traverse‐local and traverse‐regional behaviors and one map‐based traverse‐global behavior. These behaviors are integrated with a map‐based seek‐goal behavior to ensure that the mobile robot reaches its goal safely while avoiding both sensed and mapped terrain hazards. This provides a unified system in which the two independent sources of terrain quality information, i.e., prior maps and on‐board sensors, are integrated together for reactive robot navigation. The paper is concluded by a graphical simulation study. © 2003 Wiley Periodicals, Inc.     

State estimation and traversability map construction method of a quadruped robot on soft uneven terrain

Quadruped robots show excellent application prospects in complex environment detection and rescue. At present, scholars mainly focus on quadruped walking in rigid environments. However, quadruped robots often need to pass through uneven and soft unconstructed terrains, prone to slip and impact. The mismatch between the planned foothold position and the real one resulting from environmental uncertainties makes the robot unstable. In this paper, the state estimation and traversability map construction methods are proposed for quadruped robots to achieve stable walking in an unstructured environment, especially on soft terrains. First, the Error-state Kalman Filter (ErKF) is extended by optimizing the leg odometry information to get an accurate robot state, especially in soft, uneven terrain. The ErKF method fuses the sensor data from the inertial measurement unit, laser, camera, and leg odometry. The leg odometry is optimized by considering the foot slippage, which easily occurs in soft uneven terrains. Then, the unstructured environment is parameterized and modeled by the terrain inclination, roughness, height, and stiffness. A traversability map, which is essential for robot path and foothold planning in autonomous movement, is constructed with the above parameters. Finally, the proposed method is verified by simulation and experiments. The results show that the quadruped robot can walk stably on different soft and uneven terrains.     

基于地形高程模型的飞行器位姿估计方法

针对无法依靠景象匹配手段进行导航定位和无法有效利用惯导姿态信息的情况,提出了一种基于地形高程模型的飞行器绝对姿态和位置的估计方法。该方法首先利用机载下视摄像系统获取实时立体图像对及利用传感器获得飞行速度信息,通过修改双像运动模型来重建飞行器下方的地形信息;然后利用三维重建结果的刚体约束给出一种匹配机载地形高程模型数据的方法,用于估计飞行器在世界坐标系中的绝对位姿。仿真结果表明：改进的双像运动模型具有更高的精度,更有利于在世界坐标系下进行位姿估计。     

Soil parameter identification for wheel-terrain interaction dynamics and traversability prediction

This paper presents a novel technique for identifying soil parameters for a wheeled vehicle traversing unknown terrain. The identified soil parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-soil interaction model based on Composite Simpson's Rule is employed for this purpose. The key soil parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust. The identified soil parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy. The use of identified soil parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.     

基于ROAM算法的地物与地形叠加方法研究

在大规模地形实时可视化中,地物模型能否与地形模型无缝叠加将直接关系到视觉感受的逼真程度。在跨图幅分块度量ROAM算法的基础上提出了地物模型无缝叠加的算法,并引入网格索引概念,提出一种新的数据结构以快速的求取地物边界与地形三角网的交点集,根据该交点集算出属于地物范围内的多边形。实验结果表明,该算法能很好地解决地物模型与地形表面的无缝叠加问题,而且绘制速度能达到实时显示的要求。     

基于差分进化支持向量机的移动机器人可通过度预测

提出了一种移动机器人可通过度预测方法.给出了基于相对震动强度的可通度描述.通过提取典型地表图像的色彩和纹理特征并测量机器人通过该地表的相对震动强度建立训练样本集.使用差分进化算法优化支持向量机模型参数形成差分进化支持向量机对训练样本和相对震动强度进行拟合.在移动机器人运行过程中,线性分割前方地表图像形成预测子区域,通过...     

基于改进蚁群算法的越野路径规划

针对车辆的越野路径规划问题,研究并分析了地形坡度和地表属性对于车辆路径规划的综合影响。引入了“窗口移动法”对地形进行先期的坡度计算和通行性分析,就轮式车辆和履带式车辆分别建立了地表属性的粗糙度评价指标,并采用“面积占优法”将地表属性栅格化。通过建立禁忌表,叠加了坡度与粗糙度的约束影响以减少搜索范围,提高搜索效率。构造了改进蚁群算法的估价函数,并结合路径表设计了考虑坡度和粗糙度约束的路径优化算法。仿真结果表明,该算法能够快速有效地实现符合真实地形环境的越野路径规划。     

移动机器人基于多传感器信息融合的室外场景理解

本文研究了移动机器人多传感器信息融合技术,提出一种融合激光测距与视觉信息的实时室外场景理解方法．基于三维激光测距数据构建了高程图描述场景地形特征,同时利用条件随机场模型从视觉信息中获取地貌特征,并以高程图中的栅格作为载体,应用投影变换和信息统计方法将激光信息与视觉信息进行有效融合．在此基础上,对融合后的环境模型分别在地形和地貌两个层面进行可通过性评估,从而实现自主移动机器人实时室外场景理解．实验结果和数据分析验证了所提方法的有效性和实用性．     

基于改进A*算法的越野路径规划研究

针对车辆的越野路径规划问题, 研究并分析了地形坡度和地表属性对于车辆路径规划的综合影响。引入了窗口移动法对地形进行先期的坡度计算和通行性分析, 就轮式车辆和履带式车辆分别建立了地表属性的粗糙度评价指标, 并采用面积占优法将地表属性栅格化。通过建立禁忌表, 叠加了坡度与粗糙度的约束影响以减少搜索范围, 提高搜索效率。构造了改进A*算法的估价函数, 并结合expand表、open表、closed表以及path表设计了考虑坡度和粗糙度约束的路径优化算法。仿真结果表明, 该算法能够快速有效地实现符合真实地形环境的越野路径规划。     

Nonparametric Traversability Estimation in Partially Occluded and Deformable Terrain

Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long‐term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover's onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover‐terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover's attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near‐to‐far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover's attitude and configuration in partially occluded and deformable terrain.     

A method for computation of surface roughness of digital elevation model terrains via multiscale analysis

In this paper, an algorithm to compute surface roughness of digital elevation model (DEM) terrains via multiscale analysis is proposed. The algorithm employs the lifting scheme to generate multiscale DEMs. At each scale, the areas of pixels that are modified are computed. Granulometric analysis is employed to compute the average area of curvature regions in the terrain, and the average roughness of the terrain due the distribution of curvature regions. The selected case studies of the algorithm implementation demonstrated that the proposed algorithm provides a surface roughness parameter that is realistic with respect to the amplitudes and frequencies of the terrain, invariant with respect to rotation and translation, and has intuitive meaning. The algorithm allows for a good quantification of a region’s convexity/concavity over varying scales, distinguishing between shallow and deep incisions of valleys and ridges of the terrain, and hence, provides an accurate surface roughness parameter.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.