In this paper, we show how linguistic expressions can be generated to describe the spatial relations between a mobile robot and its environment, using readings from a ring of sonar sensors. Our work is motivated by the study of human-robot communication for novice robot users. The ultimate goal is to exploit these linguistic expressions for navigation of the mobile robot in an unknown environment, where the expressions represent the qualitative state of the robot in terms that are easily understood by humans. The notion of the histogram of forces was presented in previous work and used to generate linguistic descriptions of relative positions in digital images. Here, we demonstrate that it also permits fast processing of vector data and can be applied to a robot with range sensors moving in a dynamic environment. We introduce a new method for detecting partially and completely surrounded conditions, and we show that detailed descriptions can be obtained as well as coarse ones. Numerous examples are included, illustrating a variety of situations.

Automatic generation of semantic metadata describing spatial relations is highly desirable for image digital libraries. Relative spatial relations between objects in an image convey important information about the image. Because the perception of spatial relations is subjective, we propose a novel framework for automatic metadata generation based on fuzzy k-NN classification that generates fuzzy semantic metadata describing spatial relations between objects in an image. For each pair of objects of interest, the corresponding R-histogram is computed and used as input for a set of fuzzy k-NN classifiers. The R-histogram is a quantitative representation of spatial relations between two objects. The outputs of the classifiers are soft class labels for each of the following eight spatial relations: 1) LEFT OF, 2) RIGHT OF, 3) ABOVE, 4) BELOW, 5) NEAR, 6) FAR, 7) INSIDE, 8) OUTSIDE. Because the classifier-training stage involves annotating the training images manually, it is desirable to use as few training images as possible. To address this issue, we applied existing prototype selection techniques and also devised two new extensions. We evaluated the performance of di#erent fuzzy k-NN algorithms and prototype selection algorithms empirically on both synthetic and real images. Preliminary experimental results show that our system is able to obtain good annotation accuracy (92%--98% on synthetic images and 82%--93% on real images) using only a small training set (4--5 images).

Abstract—The goal of this work is to illustrate and evaluate a novel method for communicating with a mobile robot, namely, by drawing a sketch. The user draws a sketched route map to direct a mobile robot along a specified path. In this paper we focus on the navigation of the sketched path in the real environment. Challenges include sketch inaccuracies such as distortion or abstraction and low sensory resolution of the robot. Our method is based on utilizing spatial relations to extract a sequence of qualitative landmark states from the sketched map, which in turn the robot follows in a real environment to replicate the sketched route. Several examples are included. Keywords- sketch-based navigation, human-robot interaction, spatial relations, histogram of forces I.

In this paper, we provide an overview of our on-going work using spatial relations for mobile robot navigation. Using the histogram of forces, we show how linguistic expressions can be generated to describe a qualitative view of the robot with respect to its environment. The linguistic expressions provide a symbolic link between the robot and a human user, thus facilitating two-way, human-like communication. In this paper, we present two ways in which spatial relations can be used for robot navigation. First, egocentric spatial relations provide a robot-centered view of the environment (e.g., there is an object on the left). Navigation can be described in terms of spatial relations (e.g., move forward while there is an object on the left, then turn right), such that a complete navigation task is generated as a sequence of navigation states with corresponding behaviors. Second, spatial relations can be used to analyze maps and facilitate their use in communicating navigation tasks. For example, the user can draw an approximate map on a PDA and then draw the desired robot trajectory also on the PDA, relative to the map. Spatial relations can then be used to convert the relative trajectory to a corresponding navigation behavior sequence. Examples are included using a comparable scene from both a robot environment and a PDAsketched trajectory showing the corresponding generated linguistic spatial expressions. Keywords: spatial relations, linguistic spatial descriptions, mobile robot navigation, human-robot communication, histogram of forces 1.

Abstract—In this paper, we introduce our work on sketch understanding, focusing here on the analysis of a sketched route map. A route map is drawn to help someone navigate along a path for the purpose of reaching a goal. A hand-sketched route map does not generally contain complete map information and is not necessarily drawn to scale, but yet it contains the correct qualitative information for route navigation. Here we propose a methodology for extracting a qualitative model of a sketched route map, based on human navigation strategies, using spatial relationships. Linguistic descriptions are generated from the sketch, both in the form of detailed descriptions at discrete path steps and also as a high-level route description. To describe the path linguistically, one must first be able to understand the path in a qualitative sense. We assert that the translation of a sketch into linguistic descriptions illustrates that the essential qualitative path knowledge has been extracted. The methodology is demonstrated using example sketches drawn on a handheld PDA.

Abstract: Natural language requests involving vague spatial concepts are not easily communicated to a GIS because the meaning of spatial concepts depends largely on the contexts (such as task, spatial contexts, and user’s personal background) that may or may not be available or specified in the system. To address such problems, we developed a collaborative dialogue approach that enables the system and the user to construct shared knowledge about relevant contexts. The system is able to anticipate what contextual knowledge must be shared, and to form a plan to exchange contextual information based on the system’s belief on who knows what. To account those user contexts that are not easily communicated by language, direct feedback approach is used to refine the system’s belief so that the intended meaning is properly grounded. The approach is implemented as a dialogue agent, GeoDialogue, and is illustrated through an example dialogue involving the communication of the vague spatial concept near. 1

Abstract – In this paper, we discuss the use of the Scale Invariant Feature Transform to match areas between stereo images. The three dimensional location of matched points are then computed. Each matched area is further matched to a database of known objects. After projecting the three dimensional locations onto a horizontal plane, the spatial relationship between pairs of objects are then described linguistically using a system of Fuzzy rules. We are exploring the technique to facilitate human-like communication with a robot.

It is often diJficult to come up with a well-principled approach to the selection of a spatial indexing mechanism for medical image databases. Spatial information about lesions in medical images is critically important in disease diagnosis and plays an important role in image retrieval. Unfortunately, the images are rarely indexed properly for clinically useful retrieval. One example is the well-known R-tree and its variants which index image objects based on their physical locations in an "absolute" way. However, such information is not meaningful in medical content-based image retrieval systems, and the approaches above suffer from problems caused by variations in object size and shape, imprecise image centering, etc. A more appropriate approach, which does not require object registration, is to model the spatial relationships between the lesions and anatomical landmarks. To convey diagnostic information, lesions must exist in certain locations with regard to the landmarks. In this paper, we show that the histogram of forces (which represents the relative position between two objects) provides an eJficient spatial indexing mechanism in the medical domain.

A fuzzy apatial data mining technique has been developed to extract relationships describing relative position of classes of objects from raster images. Several different rule forms are described which represent different types of directional relationships between classes of objects. The method has been tested with hand-generated, synthetic, and sonar imagery.

In ongoing work on spatial relations and scene interpretation, we present a system that linguistically describes the motion of an object in a temporal sequence. This description, called the dynamic linguistic description, is inferred from a sequence of static linguistic descriptions explaining the relative position, at different instances, between a moving object and a stationary object. In this preliminary work, the moving object is assumed to be moving in a straight path at a constant velocity. The scene is monitored from a fixed pose with a constant frame rate. The proposed system is potentially useful as a lowbandwidth remote observation system capable of linguistically reporting relative position and motion in a scene.