移动机器人导航空间表示及SLAM问题研究

导航研究是移动机器人研究的承要领域之一。 空间表示则是移动机器人导航研究的基础性问题。围绕移动机器人导航空间表示,该文首先对目前广泛采用的空间分解表示,几何特征表示,拓扑地图表示等多种移动机器人导航空间表示方法进行详细的归纳和总结。通过对移动机器人导航空间各种表示疗法进行性能对比,指出各种空间表示方法的优点与不足。最后,对移动机器人导航研究中的同时定位与地图创建（SLAM）问题作了阐述,指出SLAM研究面临的问题,探讨了SLAM的未来研究方向。     

Space and time sensor fusion using an active camera for mobile robot navigation

We propose a sensor-fusion technique where the data sets for previous moments are properly transformed and fused into the current data sets to allow accurate measurements, such as the distance to an obstacle or the location of the service robot itself. In conventional fusion schemes, measurements are dependent on the current data sets. As a result, more sensors are required to measure a certain physical parameter or to improve the accuracy of a measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized to improve the measurements. The theoretical basis is illustrated by examples, and the effectiveness is proved through simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment and a structured environment.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.     

A new fusion formula and its application to continuous-time linear systems with multisensor environment

The problem of fusion of local estimates is considered. An optimal mean-square linear combination (fusion formula) of an arbitrary number of local vector estimates is derived. The derived result holds for all dynamic systems with measurements. In particular, for scalar uncorrelated local estimates, the fusion formula represents the well-known result in statistics. The fusion formula is applied to fusion of local Kalman estimates in multisensor filtering problem. Examples demonstrate high accuracy of the proposed fusion formula.     

SLAM问题中的模糊几何地图与顶点自定位法

在模糊几何地图的基础上提出了顶点定位法来解决机器人的室内SLAM中的实时自定位问题．顶点定位法是从传感信息中抽取多边形顶点作为路标进行定位．顶点定位法与传统的边匹配定位法比较有计算量小,定位精度高等优点．此外本文提出了基于空间距离的传感数据两次分类方法构建模糊几何地图,提高了数字地图精确度．实验结果表明其性能优于传统的方法．     

Dynamic localization with hybrid trilateration for mobile robots in intelligent space

In this paper, we propose a new localization algorithm based on a hybrid trilateration algorithm for obtaining an accurate position of a robot in intelligent space. The proposed algorithm is also able to estimate a position of the moving robot by using the extended Kalman filter, taking into consideration time synchronization and velocity of the robot. For realizing the localization system, we employ several smart sensors as beacons on the ceiling in intelligent space and as a listener attached to the robot. Finally, simulation results show the feasibility and effectiveness of the proposed localization algorithm compared with existing trilateration algorithms.     

震后建筑内部层次化SLAM的地图模型转换方法

由于震后建筑内部环境受损程度和震害形态分布的无法预见性, 层次化SLAM地图模型转换难以直接预设划分参数. 本文提出一种基于图形分割的区域划分方法, 实现层次化SLAM地图模型转换. 通过对机器人里程和观测信息进行图形映射, 基于信息熵生成节点集, 将环境相似度作为边的权重, 构建无向加权图及相似度矩阵; 并采用归一化割策略对图形进行划分, 得到以机器人观测视角的环境空间划分结果; 方法在解决SLAM计算量递增问题的基础上, 最小化相关信息损失, 确保全局一致性. 最后, 通过仿真及模拟废墟实验, 验证算法的有效性和可行性.     

Mobile robot localization by fuzzy logic fusion of multisensor data

Interest has been growing in the use of different sensors to increase the capabilities of navigation systems of autonomous vehicles. This area has been studied by several researchers. Nevertheless, this problem has not been solved in a fully satisfying manner. We proposed the method of global, long-term position estimation in a known environment. The method does not require any a priori position prediction or estimation. It is especially suitable in the case of using very simple sensors. The algorithm is based on fuzzy logic fusion of data received from several sensors for some time period. We discuss the methodology of solution evaluation and present some examples of typical criteria. Results of first computer simulation are presented.     

SLAM in Indoor Environments using Omni-directional Vertical and Horizontal Line Features

An autonomous mobile robot must have the ability to navigate in an unknown environment. The simultaneous localization and map building (SLAM) problem have relation to this autonomous ability. Vision sensors are attractive equipment for an autonomous mobile robot because they are information-rich and rarely have restrictions on various applications. However, many vision based SLAM methods using a general pin-hole camera suffer from variation in illumination and occlusion, because they mostly extract corner points for the feature map. Moreover, due to the narrow field of view of the pin-hole camera, they are not adequate for a high speed camera motion. To solve these problems, this paper presents a new SLAM method which uses vertical lines extracted from an omni-directional camera image and horizontal lines from the range sensor data. Due to the large field of view of the omni-directional camera, features remain in the image for enough time to estimate the pose of the robot and the features more accurately. Furthermore, since the proposed SLAM does not use corner points but the lines as the features, it reduces the effect of illumination and partial occlusion. Moreover, we use not only the lines at corners of wall but also many other vertical lines at doors, columns and the information panels on the wall which cannot be extracted by a range sensor. Finally, since we use the horizontal lines to estimate the positions of the vertical line features, we do not require any camera calibration. Experimental work based on MORIS, our mobile robot test bed, moving at a human’s pace in the real indoor environment verifies the efficacy of this approach.     

A novel trajectory-tracking control law for wheeled mobile robots

In this paper a novel kinematic model is proposed where the transformation between the robot posture and the system state is bijective. A nonlinear control law is constructed in the Lyapunov stability analysis framework. This control law achieves a global asymptotic stability of the system based on the usual requirements for reference velocities. The control law is extensively analysed and compared to some existing, globally stable control laws.     

基于声纳传感器的移动机器人地图创建方法研究*

提出了一种改进的基于声纳传感器信息进行栅格地图创建的方法。将Bayes法则用于移动机器人地图创建,对多个声纳传感器信息进行融合,解决信息间的冲突问题,并根据声纳模型将测量数据集成到局部地图中,改变栅格被障碍物占有的概率。经过坐标变换后,利用Bayes法则更新全局地图中的栅格信息,实现从局部地图到全局地图的更新。实验验证了该算法的可行性与有效性。     

基于RGB-D信息的移动机器人SLAM和路径规划方法研究与实现

移动机器人在各种辅助任务中需具备自主定位、建图、路径规划与运动控制的能力。本文利用RGB-D信息和ORB-SLAM算法进行自主定位,结合点云数据和GMapping算法建立环境栅格地图,基于二次规划方法进行平滑可解析的路径规划,并设计非线性控制器,实现了由一个运动底盘、一个RGB-D传感器和一个运算平台组成的自主移动机器人系统。经实验验证,这一系统实现了复杂室内环境下的实时定位与建图、自主移动和障碍物规避。由此,为移动机器人的推广应用提供了一个硬件结构简单、性能良好、易扩展、经济性好、开发维护方便的解决方案。     

基于信息融合的移动机器人定位与路径规划

针对镜面反射引起的声呐测距不准的问题,设计一种加权融合的方法对声呐和摄像头的信息进行融合,使移动机器人能够精确地完成在拐角区域下的自身定位,并给出了机器人的路径规划。通过在先锋3机器人平台上进行的实验结果证明,该方法能够使移动机器人安全、平滑地通过拐角区域。     

Model-based sonar localisation for mobile robots

We describe a sonar localisation system for autonomous mobile robot navigation in a known environment, which tries to extract as much information as possible from the sensors by building a detailed probabilistic model of each sonar event. It takes account of multiple hypotheses about the source of each signal and uses a probabilistic sensor fusion technique to merge the results into a single location update. The system is designed to run under our decentralised, highly parallel vehicle architecture, and we discuss some of the implementation techniques required to achieve this. The results of some initial simulations are presented.     

自主移动机器人室内定位方法研究综述

自主移动机器人的室内定位作为机器人研究领域中最基本的问题已被广泛研究。根据定位技术和传感器的不同,将室内定位方法分为航迹推算定位、地图匹配定位和基于信标定位三类。详细介绍了超声波网络定位系统和基于无线射频识别（RFID）的定位方法。对几种基于概率的定位算法做了分析和对比,并对自主移动机器人室内定位方法的研究方向做了展望。     

A hybrid control approach to action coordination for mobile robots

In this paper, the problem concerning how to coordinate the contributions from concurrent controllers, when controlling mobile robots, is investigated. It is shown how a behavior based control system for autonomous robots can be modeled as a hybrid automaton, where each node corresponds to a distinct robot behavior. This type of construction gives rise to chattering executions, but it is shown how regularized automata can be used to solve this problem. As an illustration, the obstacle-negotiation problem is solved by using a combination of a robust path-following behavior and a reactive obstacle-avoidance behavior that move the robot around a given obstacle at a predefined safety distance.     

多源融合SLAM的现状与挑战

同时定位与地图构建(simultaneous localization and mapping,SLAM)技术在过去几十年中取得了惊人的进步,并在现实生活中实现了大规模的应用。由于精度和鲁棒性的不足,以及场景的复杂性,使用单一传感器(如相机、激光雷达)的SLAM系统往往无法适应目标需求,故研究者们逐步探索并改进多源融合的SLAM解决方案。本文从3个层面回顾总结该领域的现有方法:1)多传感器融合(由两种及以上传感器组成的混合系统,如相机、激光雷达和惯性测量单元,可分为松耦合、紧耦合);2)多特征基元融合(点、线、面、其他高维几何特征等与直接法相结合);3)多维度信息融合(几何、语义、物理信息和深度神经网络的推理信息等相融合)。惯性测量单元和视觉、激光雷达的融合可以解决视觉里程计的漂移和尺度丢失问题,提高系统在非结构化或退化场景中的鲁棒性。此外,不同几何特征基元的融合,可以大大减少有效约束的程度,并可为自主导航任务提供更多的有用信息。另外,数据驱动下的基于深度学习的策略为SLAM系统开辟了新的道路。监督学习、无监督学习和混合监督学习等逐渐应用于SLAM系统的各个模块,如相对姿势估计、地图表...     

Gathering asynchronous oblivious mobile robots in a ring

We consider the problem of gathering identical, memoryless, mobile robots in one node of an anonymous unoriented ring. Robots start from different nodes of the ring. They operate in Look–Compute–Move cycles and have to end up in the same node. In one cycle, a robot takes a snapshot of the current configuration (Look), makes a decision to stay idle or to move to one of its adjacent nodes (Compute), and in the latter case makes an instantaneous move to this neighbor (Move). Cycles are performed asynchronously for each robot. For an odd number of robots we prove that gathering is feasible if and only if the initial configuration is not periodic, and we provide a gathering algorithm for any such configuration. For an even number of robots we decide the feasibility of gathering except for one type of symmetric initial configurations, and provide gathering algorithms for initial configurations proved to be gatherable.     

The multiscale directional bilateral filter and its application to multisensor image fusion

In this paper, a novel multiscale geometrical analysis called the multiscale directional bilateral filter (MDBF) which introduces the nonsubsampled directional filter bank into the multiscale bilateral filter is proposed. Through combining the characteristic of preserving edge of the bilateral filter with the ability of capturing directional information of the directional filter bank, the MDBF can better represent the intrinsic geometrical structure of images. The MDBF, which is a multiscale, multidirectional and shift-invariant image decomposition scheme, is used to fuse multisensor images in this paper. The source images are first decomposed into the directional detail subbands and the approximation subbands via the MDBF. Then, the directional detail subbands and the approximation subbands are fused according to the given fusion rule, respectively. Finally, the inverse MDBF is applied to the fused subbands to obtain the fused image. Experimental results over visible and infrared images and medical images demonstrate the superiority of our method compared with conventional methods in terms of visual inspection and objective measures.     

基于神经网络的多传感器信息融合及其在机器人中的应用

多传感器信息融合即融合多个传感器提供的冗余、互补或更实时的信息,可以获得系统所需的更准确和更精确的信息。介绍了神经网络融合方法,探讨了信息融合技术在机器人方面的应用。机器人避障实验验证了所提方法的有效性。