Wonpil Yu

Bio: Wonpil Yu is an academic researcher from Electronics and Telecommunications Research Institute. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 20, co-authored 135 publications receiving 1883 citations.

Performance Evaluation of RANSAC Family

Abstract: 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.

Environmental-Adaptive RSSI-Based Indoor Localization

Abstract: This paper addresses a novel method for localizing a stationary object in an indoor office environment. The proposed method utilizes the received-signal-strength index (RSSI) of radio signals radiating from fixed reference nodes and reference tags placed at known positions to generate a precise signal propagation model. Signal attenuation parameters are updated online according to environmental variation; thus, the proposed method has environmental-adaptation capabilities. Subsequent experiments were conducted to demonstrate the superiority of the proposed technique over a commercial location-based service (LBS) chipset.

Practical anti-vignetting methods for digital cameras

Abstract: Vignetting is a position-dependent light intensity falloff commonly found for commercial digital cameras. An anti-vignetting method is concerned with the estimation of a correction factor at each pixel position. In this paper, we propose two anti-vignetting methods which effectively estimate the distribution of correction factors. The first method utilizes wavelet-based denoising for efficient suppression of input image noise. Decimation of the smoothed distribution of correction factors is then carried out. The second method is more concerned with the appropriateness for embedded digital imaging applications. We approximate the distribution of correction factors by a single correction function, which is in the form of a 2-D hypercosine function. Only five parameters are needed to describe an underlying input intensity distribution and are estimated by nonlinear model fitting against measured input illumination data. We show the performance of the proposed methods by experimental results using synthetic and real images.

How safe are service robots in urban environments? Bullying a robot

Abstract: 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.

Localization system and method of mobile robot based on camera and landmarks

Abstract: A localization system and method of a mobile robot using a camera and artificial landmarks in a home and a general office environment (or working zone) is provided. The localization system includes artificial landmarks having an LED flash function in an invisible wavelength band, a camera with a wide-angle lens, a module flashing landmarks attached at the ceiling and identifying positions and IDs of the landmarks from an image photographed by the camera having a filter, a module calculating position and orientation of the robot using two landmarks of the image in a stop state, a module, when a ceiling to which the landmarks are attached has different heights, a position of the robot, and a module, when a new landmark is attached in the working zone, calculating a position of the new landmark on an absolute coordinate.

Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

Abstract: In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Abstract: Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

The Visual Object Tracking VOT2016 Challenge Results

Abstract: The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

Learning Background-Aware Correlation Filters for Visual Tracking

Abstract: Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient - and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers including those based on a deep learning paradigm.

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Abstract: The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.