Route following based on adaptive visual landmark matching

Route following based on visual landmark matching may require many models to cover all different situations. This paper describes a system that is able to adapt template's modelling parameters to environmental conditions (lighting, shadows, etc.) by a genetic learning technique. In addition, the mobile robot self-localisation is obtained by a stereo approach that uses the centres of matching in the two images to solve in a simple way the correspondence problem in the 3D position estimation. The experimental results show that the tracking robustness is improved, while using a small set of templates.     

Robot learning through task identification

The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics.The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal.In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc.     

A Logic-based Formalism for Reasoning about Visual Representations

This article presents a logic-based formalism for formal reasoning about visual representations. This formalism is based on previous work about describing visual notations [1]. However, in this article, we discuss major extensions to this formalism providing decidable reasoning mechanisms that support truly spatial domains such as geographical information systems (GIS). We demonstrate the application of this formalism to specifying semantics of visual query languages for GIS and to meta-reasoning about spatial queries.     

一种动态定性空间关系自动规划方法

为解决定性空间关系的规划问题,在概念邻域图的基础上提出描述动作与定性空间关系交互的邻域划分图.基于邻域划分图,提出了定性空间关系自动规划的形式化表示和推理算法,证明了算法的可靠性,并举例说明了新方法的应用.该方法在处理单方面空间关系规划中具有用通用性,在机器人导航方面具有潜在的应用前景.     

Haar invariant signatures and spatial recognition using omnidirectional visual information only

This paper describes a method for spatial representation, place recognition and qualitative self-localization in dynamic indoor environments, based on omnidirectional images. This is a difficult problem because of the perceptual ambiguity of the acquired images, and their weak robustness to noise, geometrical and photometric variations of real world scenes. The spatial representation is built up invariant signatures using Invariance Theory where we suggest to adapt Haar invariant integrals to the particular geometry and image transformations of catadioptric omnidirectional sensors. It follows that combining simple image features in a process of integration over visual transformations and robot motion, can build discriminant percepts about robot spatial locations. We further analyze the invariance properties of the signatures and the apparent relation between their similarity measures and metric distances. The invariance properties of the signatures can be adapted to infer a hierarchical process, from global room recognition to local and coarse robot localization.     

Skin cancer extraction with optimum fuzzy thresholding technique

This paper discusses a new approach to segment different types of skin cancers using fuzzy logic approach. The traditional skin cancer segmentation involves the analysis of image features to delineate the cancerous region from the normal skin. Using low level features such as colour and intensity, segmentation can be done by obtaining a threshold level to separate the two regions. Methods like Otsu optimisation provide a quick and simple process to optimise such threshold level; however this process is prone to the lighting and skin tone variations. Fuzzy clustering algorithm has also been widely used in image processing due to its ability to model the fuzziness of human visual perception. Classical fuzzy C means (FCM) clustering algorithm has been applied to image segmentation with good results; however, the classical FCM is based on type-1 fuzzy sets and is unable to handle uncertainties in the images. In this paper, we proposed an optimum threshold segmentation algorithm based on type-2 fuzzy sets algorithms to delineate the cancerous area from the skin images. By using the 3D colour constancy algorithm, the effect of colour changes and shadows due to skin tone variation in the image can be significantly reduced in the preprocessing stage. We applied the optimum thresholding technique to the preprocessed image over the RGB channels, and combined individual results to achieve the overall skin cancer segmentation. Compared to the Otsu algorithm, the proposed method is less affected by the shadows and skin tone variations. The results also showed more tolerance at the boundary of the cancerous area. Compared with the type-1 FCM algorithm, the proposed method significantly reduced the segmentation error at the normal skin regions.     

Representing and Reasoning about Motion in a Two-Dimensional World

We present a point-based spatiotemporal first-order logic for representing the qualitative and quantitative spatial temporal knowledge needed to reason about motion in a two-dimensional space. A feature of the logic is the uniform treatment of space and time. The knowledge of a simplified world, a two-dimensional street network with active traffic lights, is represented, and the reasoning problem of how a robot moves from one place to another in the world is formalized with the proposed logic.     

Incorporating decision-theoretic planning in a robot architecture

The goal of robotics research is to design a robot to fulfill a variety of tasks in the real world. Inherent in the real world is a high degree of uncertainty about the robot’s behavior and about the world. We introduce a robot task architecture, DTRC, that generates plans with actions that incorporate costs and uncertain effects, and states that yield rewards.The use of a decision-theoretic planner in a robot task architecture is demonstrated on the mobile robot domain of miniature golf. The miniature golf domain shows the application of decision-theoretic planning in an inherently uncertain domain, and demonstrates that by using decision-theoretic planning as the reasoning method in a robot task architecture, accommodation for uncertain information plays a direct role in the reasoning process.     

基于Landsat TM的城镇信息自动提取研究

为实现城镇信息的自动提取,在Erdas的Modeler模块下构建计算模型,选取大城市、小城市和乡镇各一个研究区进行实验,通过试验发现了常见的基于Landsat TM的城镇信息提取方法的不足。运用了综合阈值法进行城镇信息自动提取,条件为NDVI<0、TM1/TM4>T和TM2+TM3相似文献   

Mobile robot self-localisation using occupancy histograms and a mixture of Gaussian location hypotheses

The topic of mobile robot self-localisation is often divided into the sub-problems of global localisation and position tracking. Both are now well understood individually, but few mobile robots can deal simultaneously with the two problems in large, complex environments. In this paper, we present a unified approach to global localisation and position tracking which is based on a topological map augmented with metric information. This method combines a new scan matching technique, using histograms extracted from local occupancy grids, with an efficient algorithm for tracking multiple location hypotheses over time. The method was validated with experiments in a series of real world environments, including its integration into a complete navigating robot. The results show that the robot can localise itself reliably in large, indoor environments using minimal computational resources.     

A quadratic spline approximation using detail multi-layer for soft shadow generation in augmented reality

Implementation of shadows is crucial to enhancement of images in AR environments. Without shadows, virtual objects would look floating over the scene resulting in unrealistic rendering of AR environments. Casting hard shadows would provide only spatial information while soft shadows help improve realism of AR environments. Several algorithms have been proposed to render realistic shadows which often incurred high computational costs. Little attention has been directed towards the balanced trade-off between shadow quality and computational costs. In this study, two approaches are proposed: Quadratic Spline Interpolation (QSI) to soften the outline of the shadow and Detail Multi-Layer (DML) technique to optimize the volume of computations for the generation of soft shadows based on real light sources. QSI estimates boarder hard shadow samples while DML involves three main phases: real light sources estimation, soft shadow production and reduction of the complexity of 3-Dimensional objects’ shadows. To be more precise, a reflective hemisphere is used to capture real light and to create an environment map. The Median Cut algorithm is implemented to locate the direction of real light sources on the environment map. Subsequently, the original hard shadows are retrieved and a sample of multilayer hard shadows is produced where each layer has its unique size and colour. These layers overlap to produce soft shadows based on the real light sources’ directions. Finally, the Level of Details (LOD) algorithm is implemented to increase the efficiency of soft shadows by decreasing the complexity of vertex transformations. The proposed technique is tested using three samples of multilayer hard shadows with varying numbers of light sources generated from the Median Cut algorithm. The experimental results show that the proposed technique successfully produces realistic soft shadows at low computational costs.     

Spatial reasoning with rectangular cardinal relations

Qualitative spatial representation and reasoning plays a important role in various spatial applications. In this paper we introduce a new formalism, we name RCD calculus, for qualitative spatial reasoning with cardinal direction relations between regions of the plane approximated by rectangles. We believe this calculus leads to an attractive balance between efficiency, simplicity and expressive power, which makes it adequate for spatial applications. We define a constraint algebra and we identify a convex tractable subalgebra allowing efficient reasoning with definite and imprecise knowledge about spatial configurations specified by qualitative constraint networks. For such tractable fragment, we propose several polynomial algorithms based on constraint satisfaction to solve the consistency and minimality problems. Some of them rely on a translation of qualitative networks of the RCD calculus to qualitative networks of the Interval or Rectangle Algebra, and back. We show that the consistency problem for convex networks can also be solved inside the RCD calculus, by applying a suitable adaptation of the path-consistency algorithm. However, path consistency can not be applied to obtain the minimal network, contrary to what happens in the convex fragment of the Rectangle Algebra. Finally, we partially analyze the complexity of the consistency problem when adding non-convex relations, showing that it becomes NP-complete in the cases considered. This analysis may contribute to find a maximal tractable subclass of the RCD calculus and of the Rectangle Algebra, which remains an open problem.     

一种基于OPRA\\-4方向关系推理定性距离变化的方法

空间信息包含方向、拓扑、形状、距离等多种关系.定性空间关系表示与推理是人工智能的重要研究子域,在空间信息系统、机器人导航、自然语言理解、智能交通等领域有着广泛的应用.以往研究多面向静态空间对象,侧重单一空间关系,对不同空间关系间的约束研究不够深入,难以基于一种空间关系对另一种空间关系的演变做出有效推理.针对移动空间对象之间定性方向关系与定性距离变化的结合推理问题,利用射线与圆之间位置关系的组合来描述2个空间对象之间的相对移动方向;分别研究并证明该位置关系的组合对定性距离变化的约束作用、该位置关系的组合与粒度为4的有向点方向代数(oriented point algebra with granularity of 4, OPRA\\-4)间的对应关系,进而建立起OPRA\\-4方向关系与定性距离变化之间的内在联系;提出一种基于基本OPRA\\-4方向关系推理定性距离变化的方法,并结合交通领域中的连续k近邻查询实例说明该方法的正确性和有效性.     

The role of superior image composition in children's analogical reasoning

Analogical reasoning, as a higher cognitive ability, can help children make inferences about a novel situation. It is vital to help children's analogical reasoning development. However, the traditional intervention methods are simple and the effects cannot maintain. Aiming at this problem, the present study was the first to use computer technology especially image composition technique to promote children's analogical reasoning from an interdisciplinary perspective. Specifically, one minimum region entropy based composition model was proposed. On the one hand, sparse coding model and spatial pyramid matching model were used for searching semantically matching images. On the other hand, minimum region entropy model could contribute to composite the candidate region into an ideal position. Furthermore, we set up a database using massive images and adequate experiments based on it to verify the model's effectiveness and robustness. What's more important, we applied the improved image composition to analogical reasoning task. The results showed that the performance of intervention group was obviously better than control group during intervention stages and posttest stage. In general, the present study not only demonstrated the advantages of the improved image composition but also revealed composition's remarkable contribution for getting analogical relationship by children.     

本体辅助的定性空间关系推理机制

引入本体论思想研究空间关系描述与推理,目的是为了实现地理空间信息的共享和互操作。从地理本体的概念出发,建立了符合常识空间认知的空间关系描述模型。以河南省行政区划、交通、旅游景点为基础数据,构建了空间关系本体实例库;自定义了空间关系推理规则,并实现了规则的SWRL表达;设计了基于本体的定性空间关系推理总体框架,并结合应用实例进行了推理实验,有效地验证了空间关系本体推理机制的可行性。     

Using sign algebra for qualitative spatial reasoning about the configuration of mechanisms

This paper presents a novel approach to qualitative spatial reasoning about the spatial configuration of mechanisms that could be applied in the earlier phases of mechanism design. The basic idea in this approach is to predict all possible spatial configurations of a mechanism using sign algebra. This paper addresses spatial configuration problems of direction, position, and orientation of mechanisms. Spatial configuration matrices are introduced to describe and manipulate the spatial information of primitive mechanisms. Using these matrices, the overall spatial configurations of a complicated mechanism are constructed and then they are expressed by spatial configuration state matrices. These matrices are composed based on sign algebra and used to reason about all possible spatial solutions of a mechanism. The qualitative spatial reasoning proposed in this paper could provide useful prior information on the spatial configuration of mechanisms earlier in the conceptual design of mechanisms. The proposed approach is illustrated with a design example.     

An integrated model of autonomous topological spatial cognition

This paper is focused on endowing a mobile robot with topological spatial cognition. We propose an integrated model—where the concept of a ‘place’  is defined as a collection of appearances or locations sharing common perceptual signatures or physical boundaries. In this model, as the robot navigates, places are detected in a systematic manner via monitoring coherency in the incoming visual data while pruning out uninformative or scanty data. Detected places are then either recognized or learned along with mapping as necessary. The novelties of the model are twofold: First, it explicitly incorporates a long-term spatial memory where the knowledge of learned places and their spatial relations are retained in place and map memories respectively. Second, the processing modules operate together so that the robot is able to build its spatial memory in an organized, incremental and unsupervised manner. Thus, the robot’s long-term spatial memory evolves completely on its own while learned knowledge is organized based on appearance-related similarities in a manner that is amenable for higher-level semantic reasoning, As such, the proposed model constitutes a step forward towards having robots that are capable of interacting with their environments in an autonomous manner.     

Multi-granularity and metric spatial reasoning

Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STAR_m, CDC_m and OPRA_m. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRA_m. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRA_m with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.