Traversable Regions Research Articles

見えに基づく位置推定と３次元距離センサによる走行可能領域検出を用いた屋外移動ロボットの誘導

見えに基づく位置推定と３次元距離センサによる走行可能領域検出を用いた屋外移動ロボットの誘導

장애물 격자지도 기반 가상차선 추정 기법

Navigation in outdoor environments is a fundamental and challenging problem for unmanned ground vehicles. Detecting lane markings or boundaries on the road may be one of the solutions to make navigation easy. However, because of various environments and road conditions, a robust lane detection is difficult. In this paper, we propose a new approach for estimating virtual lanes on a traversable region. Estimating the virtual lanes consist of two steps: (i) we detect virtual road region through road model selection based on traversability at current frame and similarity between the interframe and (ii) we estimate virtual lane using the number of lane on the road and results of previous frame. To improve the detection performance and reduce the searching region of interests, we use a probability map representing the traversability of the outdoor terrain. In addition, by considering both current and previous frame simultaneously, the proposed method estimate more stable virtual lanes. We evaluate the performance of the proposed approach using real data in outdoor environments.

Toward Accurate Road Detection in Challenging Environments Using 3D Point Clouds

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.

2A1-U09 移動ロボットによる屋外広域3次元地図の生成

This reserch aims to develop a 3D map generation method by a mobile robot for outdoor environments. A robot moving around in an outdoor envirionment needs to detect obstacles and recognize traversable regions. We develop a 3D map generation method using a 3D laser range finder for a wide outdoor environment. It mainly uses an ICP algorithm for estimating ego-motions. When the ICP algorithm fails, the method uses a plane matching-based ego-motion estimation or visual odometry. When we make a map in a wide area, accumulation of ego-motion estimation errors is not negligible which will distort the resultant map. We thies applied a application of pose graph SLAM to improve the accuracy of the map.

Terrain Classification in Complex Three‐dimensional Outdoor Environments

This paper presents two techniques to detect and classify navigable terrain in complex three‐dimensional (3D) environments. The first method is a low level on‐line mechanism aimed at detecting obstacles and holes at a fast frame rate using a time‐of‐flight camera as the main sensor. The second technique is a high‐level offline classification mechanism that learns traversable regions from larger 3D point clouds acquired with a laser range scanner. We approach the problem using Gaussian processes as a regression tool, in which the terrain parameters are learned, and also for classification, using samples from traversed areas to build the traversable terrain class. The two methods are compared against unsupervised classification, and sample trajectories are generated in the classified areas using a nonholonomic path planner. We show results of both the low‐level and the high‐level terrain classification approaches in simulations and in real‐time navigation experiments using a Segway RMP400 robot.

Image Processing for Ridge/Furrow Discrimination for Autonomous Agricultural Vehicles Navigation

Autonomous navigation is important to robotize agricultural vehicles. This paper discusses a development of an image processing method to discriminate between traversable region (furrow) and non-traversable region (ridge) in ridged fields with no crops. Our method consists of 3 parts. First, images were separated into images with shade and images with no shade by gray level histograms. Next, as for images with shade, colour information was used to discriminate between traversable region and non-traversable region. On the other hand, as for images with no shade, texture was focused on. In this research, variance was used as a texture description. The proposed method was tested on images acquired by a camera, taken of different fields and under different light conditions. Our method discriminated between traversable region and non-traversable region successfully.

Real‐time path planning in heterogeneous environments

ABSTRACTModern virtual environments can contain a variety of characters and traversable regions. Each character may have different preferences for the traversable region types. Pedestrians may prefer to walk on sidewalks, but they may occasionally need to traverse roads and dirt paths. By contrast, wild animals might try to stay in forest areas, but they are able to leave their protective environment when necessary. This paper presents a novel path planning method named Modified Indicative Routes and Navigation (MIRAN) that takes a character's region preferences into account. Given an indicative route as a rough estimation of a character's preferred route, MIRAN efficiently computes a visually convincing path that is smooth, keeps clearance from obstacles, avoids unnecessary detours, and allows local changes to avoid other characters. To the best of our knowledge, MIRAN is the first path planning method that supports the aforementioned features while using an exact representation of the navigable space. Experiments show that with our approach, a wide range of different character behaviors can be simulated. It also overcomes problems that occur in previous path planning methods such as the Indicative Route Method. The resulting paths are well suited for real‐time simulations and gaming applications. Copyright © 2013 John Wiley & Sons, Ltd.

Autonomous Navigation of a Surveillance Robot in Harsh Outdoor Road Environments

This paper deals with the autonomous navigation problem of a mobile robot in outdoor road environments. The target application is surveillance in petroleum storage bases. Although there have been remarkable technological achievements recently in the area of outdoor navigation, robotic systems are still expensive due to a large number of high cost sensors. This paper proposes the reliable extraction algorithm of traversable regions using a single onboard Laser Range Finder (LRF) in outdoor road environments. The traversable regions are derived from the classifications of the road surfaces, curbs, and obstacles. The proposed scheme was experimentally tested in success. Since there are many potential applications that require autonomous service robots to move in semistructured road environments, the proposed scheme can be widely used as a low-cost practical solution.

Environmental Perception and Sensor Data Fusion for Unmanned Ground Vehicle

Unmanned Ground Vehicles (UGVs) that can drive autonomously in cross-country environment have received a good deal of attention in recent years. They must have the ability to determine whether the current terrain is traversable or not by using onboard sensors. This paper explores new methods related to environment perception based on computer image processing, pattern recognition, multisensors data fusion, and multidisciplinary theory. Kalman filter is used for low-level fusion of physical level, thus using the D-S evidence theory for high-level data fusion. Probability Test and Gaussian Mixture Model are proposed to obtain the traversable region in the forward-facing camera view for UGV. One feature set including color and texture information is extracted from areas of interest and combined with a classifier approach to resolve two types of terrain (traversable or not). Also, three-dimension data are employed; the feature set contains components such as distance contrast of three-dimension data, edge chain-code curvature of camera image, and covariance matrix based on the principal component method. This paper puts forward one new method that is suitable for distributing basic probability assignment (BPA), based on which D-S theory of evidence is employed to integrate sensors information and recognize the obstacle. The subordination obtained by using the fuzzy interpolation is applied to calculate the basic probability assignment. It is supposed that the subordination is equal to correlation coefficient in the formula. More accurate results of object identification are achieved by using the D-S theory of evidence. Control on motion behavior or autonomous navigation for UGV is based on the method, which is necessary for UGV high speed driving in cross-country environment. The experiment results have demonstrated the viability of the new method.

Roadmap Coverage Improvement Using a Node Rearrangement Method for Mobile Robot Path Planning

This paper proposes a method to efficiently abstract the traversable regions of a bounded two-dimensional environment using the probabilistic roadmap (PRM) to plan the path for a mobile robot. The proposed method uses centroidal Voronoi tessellation to autonomously rearrange the positions of initially randomly generated nodes. The PRM using the rearranged nodes covers most of the traversable regions in the environment and regularly divides them. The rearranged roadmap reduces the search space of a graph search algorithm and helps to promptly answer arbitrary queries in the environment. The mobile robot path planner using the proposed rearranged roadmap was integrated with a local planner that considers the kinematic properties of a mobile robot, and the efficiency and the safety of the paths were verified by simulation.

An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios

This paper formulates the vehicle navigation task as a constrained optimal control problem with constraints bounding a traversable region of the environment. A model predictive controller iteratively plans an optimal vehicle trajectory through the constrained corridor and uses this trajectory to establish the minimum threat posed to the vehicle given its current state and driver inputs. Based on this threat assessment, the level of controller intervention required to prevent departure from the traversable corridor is calculated and driver/controller inputs are scaled accordingly. Simulated and experimental results are presented to demonstrate multiple threat metrics and configurable intervention laws.

Compact Modeling Technique for Outdoor Navigation

In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.

Traversable Region Modeling for Outdoor Navigation

This article presents a new methodology to build, in real-time, compact local and global maps for outdoor navigation. The environment information is obtained from a 3D laser range. The navigation model, called Traversable Regions Model (TRM), is based on Voronoi diagram technique but adapted to large outdoor environments, that is, the model is built from 3D data. In the manuscript we also present a novel contribution to the regions modeling field in robotics. The method allows to calculate the roughness degree of an unknown terrain based on the normal vector deviation. The parameter which measure the roughness degree is called spherical variance and it will be useful to determine the traversable areas. The model built allows defining safe trajectories depending on the robot's capabilities and the terrain properties and will represent, in a topo-geometric way, the environment as local and global maps. The methodology presented is validated in real outdoor environments with an outdoor robot developed in our lab, called $$\mathcal{G}\mathcal{O}\mathcal{L}\mathcal{I}\mathcal{A}\mathcal{T}$$ .

傾斜平面ステレオによる走行可能路面と障害物の検出

For autonomous mobility of unmanned or tele-operated vehicles, the visual navigation capabilities such as obstacle detection and terrain estimation are essential. This paper presents a robust model-based approach for detecting traversable regions and obstacles even in off-road environments. In our approach, a model-based polynocular stereo, which we call tilted-plane-based stereo, is utilized. We newly define the algorithm by using a group of slant parallel planes to be searched in the spatial disparity space. It is capable of directly measuring height differences in the view field. By checking abrupt jumps in height in the 3D space, traversable regions of the terrain can be effectively estimated. Then, the obstacles belong to the untraversable regions are robustly detected. We show successful results of the proposed method for different obstacles in the real scenes.

An intelligent terrain-based navigation system for planetary rovers

A fuzzy logic framework for onboard terrain analysis and guidance towards traversable regions. An onboard terrain-based navigation system for mobile robots operating on natural terrain is presented. This system utilizes a fuzzy-logic framework for onboard analysis of the terrain and develops a set of fuzzy navigation rules that guide the rover toward the safest and the most traversable regions. The overall navigation strategy deals with uncertain knowledge about the environment and uses the onboard terrain analysis to enable the rover to select easy-to-traverse paths to the goal autonomously. The navigation system is tested and validated with a set of physical rover experiments and demonstrates the autonomous capability of the system.