One of the main problems of binary classification of overlapping distributions is that there always exist misclassification errors with any value of threshold. In this paper, we propose a novel lazy decision approach for robust object detection and tracking, where decision on an uncertain observation whose evaluation lies between low and high thresholds is postponed until a clear evidence appears. As a practical application of the proposed approach, we present a sensor fusion pedestrian detection system for safe navigation of UGVs in driving environment. We combine a laser-based detection of target candidates and vision-based evaluation within the proposed lazy decision framework. Experimental results on real test data demonstrate effectiveness of the proposed approach, showing significant improvement of precision-recall performance.

3D range sensors are currently being used in various fields. Creating 3D range sensors requires various techniques, such as object detection, tracking, classification, 3D SLAM, etc. For the pre-processing step, superpixels can improve the performance of these techniques. This paper proposes a novel over-segmentation algorithm, known as superpixels, for 3D outdoor urban range data. Superpixels are generated with three steps: boundary extraction using a surface change score and sensor models, initial cluster seeding using a quadtree decomposition, and iterative clustering, which adapts a k-means clustering approach with limited search size in the quadtree dimension. The proposed algorithm produces adaptive superpixel sizes that take into account surface and object border information. This reduces memory size more than regular grid methods and represents small objects well with adaptable pixel sizes. The algorithm is verified using the publicly available Velodyne dataset and the manually annotated ground truth. A comparison with the conventional algorithm is also presented.

This paper presents an efficient method for building a topological map for robots in urban environments based on satellite image maps acquired from a geographic information system (GIS). In urban space, mobile robots need a special map, such as a topological map, to generate a path toward their goal. A topological map for mobile robot navigation should include semantic data, e.g., the width and type of road. We divide the satellite image based topological map building process into two steps. The first step is defining the topological map model to fit the mobile robot navigation in an urban environment. The second step is generating a topological map from existing satellite image maps downloaded from the GIS to reduce the cost and improve the accuracy in building the map. If a GIS-based topological map is constructed once, it is possible to use the map for the navigation of various robots as common spatial data. To validate the proposed mapping method, we show details of its implementation in map building as well as in a navigation experiment in real outdoor environments, such as a campus or city district.

A* on grid maps generates a path with zig-zag pattern, but Theta* is known to be free from this disadvantage. Theta* assumes that cost of each cell, cell-cost, is uniform, but non-uniform costs are effective ways to represent traversability on grid maps. Theta* does not work on non-uniform costmaps. In this paper, we generalize Theta* toward non-uniform costmaps. To extend Theta*, we propose two kinds of cost functions considering non-uniform cell-costs. The first function adopts the arithmetic mean under the assumption that all cells contribute equally to the overall cost. The second function uses the weighted mean by considering the true traversal length on each cell. We applied the proposed methods to two types of maps: synthetic and real maps. An experiment on synthetic maps quantifies performance of the two methods in terms of accuracy and computing time. The other experiment on real maps presents the effectiveness of Theta* with the proposed methods. The generalized Theta* generated the least-cost path compared with the original Theta* and A* on non-uniform costmaps.

This paper presents a localization method in urban environments by using dead reckoning sensors, Global Positioning System (GPS), and taking into account the benefits of map matching. Extended Kalman Filter (EKF) is used as the main framework to fuse the information from sensors. However, the result of the EKF greatly depends on how the robot utilizes and judges the position measurement which comes from GPS since the GPS easily gives wrong position measurement due to the phenomenon called multipath effect. Under the assumption that the robot must operate only on the main road, a map matching is used to filter out the wrong GPS measurements which fall outside the main road. An experiment has been conducted in urban environment to validate the proposed method. Experimental results show that our proposed method has superior performance compared to the EKF without map matching.

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person's daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the others from videos (e.g. accident, overtaking, ...), and labels them together with its contextual knowledge on the vehicle (e.g. its physical location on the map) to construct an event log. A novel decision tree based activity recognizer that incrementally learns driving events from first-person view videos is designed. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the user to retrieve videos of dangerous situations and analyze his/her driving habits statistically. Our experiment confirms that the proposed system reliably generates driving diaries by annotating learned vehicle events.

In this paper, we present a novel human activity recognition approach that only requires a single video example per activity. We introduce the paradigm of active video composition, which enables one-example recognition of complex activities. The idea is to automatically create a large number of semi-artificial training videos called composed videos by manipulating an original human activity video. A methodology to automatically compose activity videos having different backgrounds, translations, scales, actors, and movement structures is described in this paper. Furthermore, an active learning algorithm to model the temporal structure of the human activity has been designed, preventing the generation of composed training videos violating the structural constraints of the activity. The intention is to generate composed videos having correct organizations, and take advantage of them for the training of the recognition system. In contrast to previous passive recognition systems relying only on given training videos, our methodology actively composes necessary training videos that the system is expected to observe in its environment. Experimental results illustrate that a single fully labeled video per activity is sufficient for our methodology to reliably recognize human activities by utilizing composed training videos.

Although GPS/DGPS become the dominant localization solution in the outdoor environment, it needs assistant sensors or algorithms for the covering the area not to get the position information from GPS. Especially, in the robot navigation, the sensor fusion algorithm is needed. In addition, it is hard to get the position information at the area surrounded the high buildings such as the downtown because GPS signals is so feeble. Therefore, this paper illustrates an efficient method for the outdoor localization incorporating DGPS, Encoder, and IMU sensor based on EKF. To show the localization performances of the proposed fusion algorithm, we have implemented the proposed algorithm and applied the advertising robot platform which is operating well during 80 days in the real semi-outdoor structured environment. The proposed sensor fusion algorithm and the experimental results showed the feasibility of our novel sensor fusion algorithm.

This paper describes and discusses the preliminary results of a behavioural study on robot social acceptability, which was carried out during a public demonstration in South Korea. Data was collected by means of direct observation of people behaviour during interaction with robots. The most interesting result to emerge is that of young people: they tended to react to the robots presence with extreme curiosity and, quite often, to treat them aggressively. In this paper, the word bullying is used to describe any kind of improper and violent behaviour, intended to cause damages or impede the robot operation. It is the authors' opinion that if not tackled appropriately, abuses towards robots may become a serious hindrance to their future deployment, and safety. Hence, the necessity to tackle this issue with dedicated solutions during the early phases of design.

This paper proposes the artificial landmark map building method using a Grid-based Simultaneous Localization And Mapping (Grid SLAM). The Grid SLAM method is employed to simultaneously localize the position of mapping cart and construct the map of working area. Based on the estimated position of the mapping cart and the grid-based map, the artificial landmarks are localized and their positions are saved to the artificial landmark map. The proposed method reduces the complexity and the cost of mapping process which is usually done by hand. The real implementation has been carried out to build the artificial landmark map of large scale indoor environment named T-City. The implementation results show that the proposed method gives a convenient way to construct the artificial landmark map while still maintaining the accuracy of map. The correctness of the artificial landmark map has been confirmed through the real operation of the mobile robots which rely on the artificial landmark map for their navigation at T-City.

RANSAC (Random Sample Consensus) has been popular in regression problem with samples contaminated with outliers. It has been a milestone of many researches on robust estimators, but there are a few survey and performance analysis on them. This paper categorizes them on their objectives: being accurate, being fast, and being robust. Performance evaluation performed on line fitting with various data distribution. Planar homography estimation was utilized to present performance in real data.

The core step of video stabilization is to estimate global motion from locally extracted motion clues. Outlier motion clues are generated from moving objects in image sequence, which cause incorrect global motion estimates. Random sample consensus (RANSAC) is popularly used to solve such outlier problem. RANSAC needs to tune parameters with respect to the given motion clues, so it sometimes fail when outlier clues are increased than before. Adaptive RANSAC is proposed to solve this problem, which is based on maximum likelihood sample consensus (MLESAC). It estimates the ratio of outliers through expectation maximization (EM), which entails the necessary number of iteration for each frame. The adaptation sustains high accuracy in varying ratio of outliers and faster than RANSAC when fewer iteration is enough. Performance of adaptive RANSAC is verified in experiments using four images sequences.

This paper describes an enhanced haptic assembly simulation system, in which an optimal assembly algorithm is used to allow haptic interactions and traditional assembly sequence problems. The optimal assembly algorithm provides optimal paths for haptic guidance as well as an assembly sequence of the parts to be assembled. The performance of the given assembly schemes were simulated and analyzed using a haptic assembly system. Experimental results showed that the haptic-path sequence-guidance (HSG) mode gave the best performance improvement in terms of accumulated assembly time (28.56%) and travel distance (15.64%) compared to the unguided mode, while the sequence-guidance (SG) mode alone increased performance by 16.91% for assembly time and 11.66% for travel distance. The experimental results were analyzed by the sub-tasks of gripper selection, inter-part movement, and part assembly which showed the effectiveness of the optimal assembly algorithm.

In this paper, we propose a system which reconstructs the environment with both color and 3D information. We perform extrinsic calibration of a camera and a LRF (laser range finder) to fuse 3D information and color information of objects. We also formularize an equation to measure the result of the calibration. Moreover, we acquire 3D data by rotating 2D LRF with camera, and use ICP (iterative closest point) algorithm to combine data acquired in other places. We use the SIFT (scale invariant feature transform) matching for the initial estimation of ICP algorithm. It offers accurate and stable initial estimation robust to motion change compare to odometry. We also modify the ICP algorithm using color information. Computation time of ICP algorithm can be reduced by using color information.

A simple but effective method for fast path finding for mobile robots in grid-based search space is presented. The paper presents a systematic way to reduce the resolution of a grid map preserving the occupancy structure of the original map. Paths are found in two steps with varying quantization levels of the space in coarse-to-fine manner. The resulting paths are practically optimal and experimental results show a great reduction of the searching time, making it possible to implement a real-time global path planner in a very large environment.

This paper presents a method to build the large-scale indoor maps by means of extended Kalman filter (EKF) localization which explores the statistical distribution of noise parameters. As typical method in many robot localization applications, EKF localization has shown considerable success history to locate the position of the robot. However, EKF has also lack which can degrade its performance, especially in the real environment due to incompleteness, incorrectness and imprecision of noise parameters. Moreover, although many kinds of sensors are used for EKF localization, it is still difficult to generate an accurate map because of noise parameters. The fundamental solution of this problem should be addressed to the utilization of adequate noise parameters setting. We have developed a new technique for searching the optimal noise parameters setting of EKF localization using a statistical distribution. The experiments carried out on mobile robot have been performed to build accurate maps by using EKF localization relied on statistical distribution of noise parameters. The mapping results show that the method based on statistical distribution can be useful for practical application.

This paper describes a concerted effort to design and implement a robotic service framework. The proposed framework is comprised of three conceptual spaces: physical, semantic, and virtual spaces, collectively referred to as a ubiquitous robotic space. We implemented a prototype robotic security application in an office environment, which confirmed that the proposed framework is an efficient tool for developing a robotic service employing IT infrastructure, particularly for integrating heterogeneous technologies and robotic platforms.

We propose a network robot application utilizing a human-in-the-loop structure. The proposed structure hybridizes robot perception with perception capability of a human, rendering a robot immune to the uncontrolled environment. Network infrastructure enables the user to intervene in the operation of a robot whenever necessary, enabling seamless transition of task control between the robot and the remote user. We have developed a prototype security robot system by implementing the proposed human-in-the-loop structure. Introduction of network infrastructure, however, also invokes system instability due to network fluctuation, time delay, or jittering. We could resolve the instability by imparting task intelligence to the robot and employing wireless distribution system (WDS) configuration for seamless data communication beyond the coverage of a single access point. Although our approach may solve only part of problems inherent in networked robots, we found that the developed robot system bears sufficient applicability to real world robotic systems incorporating large-area robot navigation.

This paper outlines research motivations of ubiquitous robotic space (URS) and provides concepts of wireless localization networks, which are used for the navigation of indoor network robots and pedestrian. As components of URS localization system, this paper introduces a calibration algorithm, coarse and fine localization systems, and three key hardware equipments developed: StarLITE, Navi-guider, and UMVC. The StarLITE is a IR-LED sensor, Navi-guider is a tethering system used for robot guidance, and UMVC is a multi-view camera. Based on an experimental test, it will be shown that a UWB-based localization system can be reliably used for the navigation of mobile robots and pedestrian in the ubiquitous robotic space (URS).

This study introduces the sonar quick mapping method using tethered-robot guiding system, Navi-guider, that is one of the human and robot interactive devices. Basic function of the Navi-guider is to actively control the robot through the tension length and an orientation of the cable. This is able to easily control the mobile robot, is applied to robot controller for quick mapping. Using Navi-guider, user can easily control the mobile robot, but the sonar sensors of the robot head occasionally make the wrong decision to return the incorrectly measured distance between the sensor and an object. Therefore, this paper presents a Altering method, called a cell conjunction Alter (CCF), for building a sonar grid-based map for a mobile robot using Navi-guider. The CCF can recover the wrongly updated map by the incorrect sonar data. The principle of the CCF was based on revaluation of the occupancy possibility in a certain cell through the probabilistic relation among the cells. In this paper, the CCF was applied to a basic grid map construction model, Bayesian model, to verify its effectiveness. Experimental results were also obtained using a mobile robot in a real world environment.

The Navi-Guider, which is a new tool to make it easy to handle mobile robots, is presented in this paper. Since a robot used in home should be operated easily, the proposed robot control scheme used for the Navi-Guider has to be designed to support the user-interface. The Navi-Guider that is easily mounted in a mobile robot could detect a length and a direction of the tether pulled out by a user. Those detected factors are utilized in the Navi-Guider for the robot guidance and control.

In this paper, we present a wireless localization technique which operates in an asynchronous manner. We also state how to improve the system's performance using internal navigation sensors. The wireless localization system is developed based on the difference of signal's arrival times, so-called inter-node time difference-of-arrival (ITDOA). The advantage of this ITDOA-based localization scheme is that it uses the time differences of each node (not between nodes); so the time synchronization is not required. However, sometimes far more accurate location information than that of ITDOA localization system is needed. To increase the localization accuracy of ITDOA system, we use heading angle information provided from an internal heading sensor. The simulation result shows that the accuracy of the object position estimated from internal sensor aided ITDOA system is improved about 49% compared to the ITDOA system.

The localization is one of the most important issues for mobile robot. We describe a novel localization sensor suite for the development of a wireless location sensing network. The sensor suite comprises wirelessly controlled infrared landmarks and an image sensor which detects the pixel positions of infrared sources. The proposed sensor suite can operate irrespective of the illumination condition in the indoor environment. We describe the operating principles of the developed sensor suite and report the performance for mobile robot localization and navigation. The advantage of the developed sensor suite lies in its robustness and low cost to obtain localization information as well as simplicity of deployment to build a robot location sensing network. Experimental results show that the developed sensor suite outperforms the state-of-the-art localization sensor

This paper describes camera based character recognition system, which is implemented for mobile devices such as PDA and cellular phones with color cameras. First, we have developed camera based character recognition system for PC that includes techniques such as image enhancement, local adaptive binarization and blob coloring to effectively extract character region and remove the noise of camera captured images. Then, we converted the PC based OCR system to the embedded OCR system for cellular phones. Several functions were specially developed since most of mobile telecommunication devices have no real number computing functions and have limitation of memory space. In this paper, we are addressing the related problems and our system mechanisms.

This paper describes a new lens distortion correction method suitable for consumer digital imaging and consequent mobile computing applications. First, a convenient way of camera calibration for lens distortion coefficient estimation is proposed with experimental validation. An image warping method for lens distortion removal is formulated by inverse spatial mapping; subsequently, an approximation scheme is proposed which is adapted for real-time application. Finally, the method is extended such that the calibration stage can be completely removed. Experimental results show that the proposed method effectively replaces costly calibration process and provides a distortion-free quality image.

This paper presents a simple method for the calibration of a focal length and rotation parameters of a camera. We assume that the remaining calibration parameters of the camera change so little that they are considered fixed constants during a visual task. The employed vision system comprises a pan/tilt mechanism and a single camera, which is a typical setup for mobile robot navigation. The calibration process is performed by making use of vanishing points obtained from a rectangular pattern with an unknown size. Determination of vanishing points relies on a viewpoint invariant property in perspective transform-cross ratio. Focal length calibration is carried out by inner product of two vanishing point vectors. The rotation parameters are also estimated by projecting the image plane on the Gaussian sphere and extracting the surface normal vector of the calibration object. Possible errors in the calibrated parameters are studied by simulation and a preliminary result is presented.

Proposes a novel local homing algorithm for indoor mobile robot navigation. In the algorithm, we divide the whole navigation task into simple local tasks in order to reduce the computational burden and the required memory size. We develop a new environment model based on the omnidirectional sensor data obtained from the Omnidirectional Range and Intensity Sensing System (ORISS), which consists of a set of ultrasonic sensors and a vision sensor. In order to enhance the reliability of the sensor information, we fuse the sensor data by means of the characteristics of the indoor environment structure and the sensor model. To verify the proposed algorithm, experiments with a mobile robot are carried out in a corridor.