In recent years, high-dimensional descriptive features have been widely used for feature-based robot localization. However, the space/time costs of building/retrieving the map database tend to be significant due to the high dimensionality. In addition, most of existing databases are working well only on batch problems, difficult to be built incrementally by a mapper robot. In this paper, a scalable localization algorithm is proposed for incremental databases of high dimensional features. The Monte Carlo localization (MCL) algorithm is extended by employing the exact Euclidean locality sensitive hashing (LSH). The robustness and efficiency of the proposed algorithms have been demonstrated using the radish dataset.

Vehicle relocation is the problem in which a mobile robot has to estimate the self-position with respect to an a priori map of landmarks using the perception and the motion measurements without using any knowledge of the initial self-position. Recently, random sample consensus (RANSAC), a robust multi-hypothesis estimator, has been successfully applied to offline relocation in static environments. On the other hand, online relocation in dynamic environments is still a difficult problem, for available computation time is always limited, and for measurement include many outliers. To realize real time algorithm for such an online process, we have developed an incremental version of RANSAC algorithm by extending an efficient preemption RANSAC scheme. This novel scheme named incremental RANSAC is able to find inlier hypotheses of self-positions out of large number of outlier hypotheses contaminated by outlier measurements

Mobile robot teleoperation is a method for a human user to interact with a mobile robot over time and distance. Successful teleoperation depends on how well sensor images taken by the mobile robot are visualized to the user. To enhance the efficiency and the flexibility of the visualization, an image retrieval system on such a robot's image database would be very useful. The main difference of this image retrieval problem from the standard one is that due to variety of viewing conditions, there exist various of relevant images. The main contribution of this paper is to propose an efficient retrieval approach for such a problem by utilizing correlation between visual features and real world locations of images. An active learning technique based on support vector machine is extended for this aim. The second contribution is to present a multi-hypothesis approach to deal with location uncertainty inherent in real world applications

Image based rendering (IBR) is one of standard approaches to view synthesis. IBR would be very important for human-robot interface system (HRI) such as a teleoperation system, since the synthesized views are helpful for a human user to understand the robot's operating environment. In this paper, we address the problem of IBR on images taken by a mobile robot. Main difficulty of our problem is that the viewpoint locations of input (real) images are not precisely known, due to estimation errors inherent in the positioning systems. To solve this problem, we will propose a novel IBR method, where the location uncertainty is reduced using a visual landmark, which is commonly used in mobile robotics. In addition, novel priors on real images are introduced to regularize the IBR problem. As a result, IBR can be performed successfully under the location uncertainty.

A set of images taken by a mobile robot during navigation can be viewed as a large-scale image database, content-based image retrieval (CBIR) is an effective approach to image retrieval on such large-scale image databases. For the aim of human-robot interface, CBIR on the robot image database would be very useful, to train the robot to learn features (e.g. color, texture) of the relevant objects (e.g. book, chair) that a human user is interested in. To achieve CBIR, a small but sufficient member of relevant objects' features are required. We will propose a novel CBIR method that efficiently searches such relevant objects based not only on their features, but also on their locations with respect to the robot's workspace. To combine both of the feature and the location information, a novel 2D occupancy map called query based occupancy map (QOM) is presented.

This paper discusses a range finder in which binary pattern masks apply to its image sensing device. The range finder uses the slit-ray projection method. Its image sensing device can quickly detect positions of slit-ray images on it by using the binary pattern masks. A Prototype range finder has been developed, the image sensing device of which has 32/spl times/32 photo diodes. As a result of calibration, it has accuracy within /spl plusmn/0.9%. In this paper, first, the image sensing device and pattern masks is explained. Then the prototype range finder and the procedure of calibration are described. Finally, some of the results of measurements are shown and future works are indicated.

In this paper, we propose a method for global localization in non-stationary environments, where the environment is partially or completely different from the map. We assume there is no moving object. It is difficult to detect changes when both of the self-position and the map have large uncertainties. To solve the problem, we extended Monte Carlo Localization (MCL) so as to generate a number of hypotheses about the change as well as the self-position. We also introduced Sensor Resetting Localization (SRL), in order to generate initial estimation of self-position, or to recover from large positioning errors. The proposed method has been tested in a number of environments as well as changes. As a results, we found the proposed method is effective even when "Rate Of Changes (ROC)" is high in the environment.

Our goal is to develop a new vision system for detecting mobile robots and their motion sequences in office environments. The vision system watches a sequence of images acquired by the vision sensor, as well as a sequence of robot's command transmitted via a wireless network. To identify the robot, a sequence of motion is extracted for a moving object in the image sequence, and compared with the sequence of command. Simple features are used and extracted with various types of methods for detecting moving objects. This simplicity helps improve the robustness against unpredictable uncertainties inherent in the environments. In. order to improve recognition rate per frame, the vision system interacts with the robot, to learn features of the robot. A support vector machine (SVM) is used for this learning. In addition, the system is optimized reflecting characteristics of the robot and the environment.

We propose a vision-based method for tracking multiple persons with gray-scale image sequence acquired by a monocular vision sensor in cluttered office environments. This method is based on a novel algorithm for acquiring depth of targets with primitive and robust features, position and size of targets. To cope with long-term occlusions caused by both fixed and moving objects, the method memorizes and utilizes history data of targets' state. We employ MCMC-based particle filter (MCMC-PF) to implement such domain knowledge including, interactions between targets, as well as radial reach filter (RRF) to extract objects in noisy gray-scale images. In experiments, the method could track multiple persons reliably, and recover from errors even when it loses sight of targets.

In recent years, RFID (radio frequency identification) system has become very popular in service industries, logistics and manufacturing, as it is an inexpensive and reliable device for automatic identification. Therefore, RFID system would be useful in a problem of mobile robot self-localization, if tags are distributed in the environment, and if the robot is equipped with a RFID reader to communicate with the tags. In this paper, we propose a novel method for learning-based localization with a RFID system by using support vector machine (SVM). In order to obtain various training data for SVM learning, a number of synthesized sensor data are generated from limited amount of real sensor data. We also propose a method that enables a user to easily place tags in effective locations. In experiments with a mobile robot, the performance of the proposed method is demonstrated.

One of the fundamental problems of the mobile robots is self-localization, i.e., to estimate the self-position by comparing sensor data and a map. In non-stationary environments, a robot should avoid to use changed objects as landmarks in the localization. However, in most previous localization methods, it is assumed that there is no change, or changes are easily identified by sensing. In this paper, we propose a self-localization method that is robust against changes in environments. The method identifies changes from noisy and ambiguous sensor data. Since an object with a random shape may be added at a random position, it generates and utilizes multiple hypotheses about the changes. A number of simulation experiments have been performed in various environments, to demonstrate the effectiveness of the method.

This paper describes a new fuzzy satisfaction method using genetic algorithms (GA) for multiobjective problems. First, an unsatisfying function, which has a one-to-one correspondence with the membership function, is introduced for expressing "fuzziness". Next, the multiobjective design problem is transformed into a satisfaction problem of constraints by introducing an aspiration level for each objective. Here, in order to handle the fuzziness involved in aspiration levels and constraints, the unsatisfying function is used, and the problem is formulated as a multiobjective minimization problem of unsatisfaction ratings. Then, a GA is employed to solve the problem, and a new strategy is proposed to obtain a group of Pareto-optimal solutions in which the decision maker (DM) is interested. The DM can then seek a satisfaction solution by modifying parameters interactively according to preferences.

To construct a 3D model of an object in a computer, a range finder is used to obtain range images of the object. The range finder takes range images from many view points to eliminate the influence of occlusions. A registration algorithm is, then, used to merge the range images. When the range finder is mounted on a mobile robot and measures while moving, range images taken by it will be distorted. We extended the registration algorithm to remove the deformation. The extended algorithm can remove it and is used to construct a 3D model even for a case where the speed parameters of the robot are not well known.