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The purpose of this paper is to provide algorithms of the automatic landmark extraction software program that are applicable for any torso shape.

In this study, Automatic Landmark Identification (AULID), an automatic landmark extraction software program, was developed to extract consistent landmark locations from any torso shape. A methodology of geometrical characteristics of the body surfaces around each landmark was used for the algorithms and implemented with C++. The accuracy of the AULID was tested on various torso shapes. The verification methodology consisted of mean difference (MD), mean absolute differences (MAD), and one‐way analysis of variance. Duncan test for multiple comparisons was used to evaluate the significant differences of MAD values among different torso groups. The MAD values were compared to the anthropometric survey allowable errors.

The algorithms of AULID provided both accuracy and consistency of identifying landmarks on any body torso types.

Most 3D body scanning systems often show landmark location errors when dealing with nonstandard body shapes. None of automatic landmark extraction software program provides consistency of identifying landmarks in various body shapes. However, algorithms of AULID, an automatic landmark extraction software program, in this study are only consistent definitions for identifying landmarks in any torso shape.

This paper aims to present a novel localization scheme using infrared light reflecting artificial landmarks.

By putting the infrared light reflecting landmarks on the ceiling, localization is achieved. The landmark is designed for effective recognition and identification. From the difference of two successive images, one with the infrared light illumination and the other without, the landmarks are clearly identified.

From the mathematical analysis, a greater of landmarks are required if the robot's tilt angles are not known. With the camera's pan/tilt angles information, the distortion of the image can be corrected and fewer landmarks are required. Movement of the camera while getting two successive images is modeled and is compensated.

Thorough analysis of practical issues such as capturing the image from a non‐flat floor, pan/tilt motion of the camera and movement of the camera is presented.

FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms.

As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates.

Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy.

The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.

The purpose of this research is to examine the effect of temporal landmarks on positive illusions and the downstream implications of this effect on consumer preference for new products with functional risks.

Study 1 adopted a single factor (temporal landmarks: beginning vs ending) between-subjects design. Study 2 adopted a 2 (temporal landmarks: beginning vs. ending) × 2 (salience of the temporal landmark: salient vs not salient) between-subjects design. Study 3 used a single factor (temporal landmarks: beginning vs ending) between-subjects design.

Through three studies, we show that the ending temporal landmarks reduce positive illusions (Studies 1 and 2). The underlying process is enhanced perceptions of psychological resource depletion (Study 3). The authors further show that decreased positive illusions lead consumers to less prefer new products with functional risks (Study 3).

Existing studies on temporal landmarks have exclusively focused on the beginning landmarks and account for its effects from a motive perspective. In contrast, the authors take a look at the ending landmarks and identify perceptions of psychological resource depletion as the underlying process, which suggests a new angel understand how temporal landmarks influence individuals' cognitions and behavior.

The purpose of this paper is to propose a distributed protocol to build a logical coordinate system based on the hop counts of each node to four selected landmarks, and the real location information is not needed.

The designed protocol uses the sink node as one of the landmarks and then selects three other sensor nodes near the corners of the sensor network as landmarks.

The simulation results show that the proposed protocol has the superior performance in packet delivery ratio, average hop counts among nodes, and communication overhead to previous works.

This paper presents a distributed protocol to build a logical coordinate system based on hop counts to landmarks.

This article aims to analyze the performance and risk of landmark building in the housing sector and to evaluate their usefulness for a diversification strategy.

After comparing summary statistics on the performance of landmark building with respect to other types of housing investments, the article evaluates their usefulness for a diversification strategy. The role of landmark buildings is studied using the modern portfolio theory and evaluating the role of this type of asset in the optimal asset allocation. The analysis is performed considering both the risk/return trade-off in a one-year and a multiple-year time horizon.

The results show that a landmark building can be a good investment opportunity, especially for high-risk/return investors. A not perfect correlation of the returns of this asset class with other types of housing investments implies the existence of a minimum investment in this asset class for almost all portfolios on the efficient frontier. Results are robust with respect to the length of the investment time horizon.

The article presents a unique analysis of intra-housing market diversification opportunities focusing on the role of landmark building in the portfolio construction. Empirical evidence supports the hypothesis that real estate investors can take advantage of investing in landmark buildings in the residential sector as well because there are no reasons to limit such investments to trophy buildings in the office and commercial sectors.

Landmark‐based navigation of autonomous mobile robots or vehicles has been widely adopted in industry. Such a navigation strategy relies on identification and subsequent recognition of distinctive environment features or objects that are either known a priori or extracted dynamically. This process has inherent difficulties in practice due to sensor noise and environment uncertainty. This paper is to propose a navigation algorithm that simultaneously locates the robots and updates landmarks in a manufacturing environment. A key issue being addressed is how to improve the localization accuracy for mobile robots in a continuous operation, in which the Kalman filter algorithm is adopted to integrate odometry data with scanner data to achieve the required robustness and accuracy. The Kohonen neural networks have been used to recognize landmarks using scanner data in order to initialize and recalibrate the robot position by means of triangulation when necessary.

The purpose of this paper is to describe a general purpose localization system, GRAIL. GRAIL provides real‐time, adaptable, indoor localization for wireless devices.

In order to localize as diverse a set of devices as possible, GRAIL utilizes a centralized, anchor‐based approach. GRAIL defines an abstract data model for various system components to support different physical modalities. The scalable architecture of GRAIL provides maximum flexibility to integrate various localization algorithms.

The authors show through real deployments that GRAIL functions over a variety of physical modalities, networks, and algorithms. Further, the authors found that a centralized solution has critical advantages over distributed implementations for handling privacy concerns.

A key contribution of this system is its universal approach: it can integrate different hardware and software capabilities within a single localization framework. The deployment of such a system in academic and research environments allows researchers to explore issues beyond algorithms and investigate effects in real deployments.

To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM.

SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation.

Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system.

The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.

The three‐dimensional (3D) body scanner is an important new technology that will impact the design and production of apparel, but use of this tool is at an early stage of development. Appropriate measurement extractions from the complex 3D scans that will address the needs of apparel patternmakers are an essential part of the development process for this new tool. The paper aims to address these developments.

In this study, a method of automatically locating the side seam for torso fitting garments from 3D body scans for a variety of body types was developed and tested. The method is based on the location of center points of body depth measurements, and five different body landmarks or combinations of body landmarks were tested to determine the best choice for implementation.

Based on rankings and ratings of the results by apparel experts, a method using the average value of body depth measurements taken at about 100 locations equally spaced from the axilla to the crotch was chosen as the best solution.

Additional testing of this method and development of a method for locating the side seam for lower body garments is the next step in this research.

Identifying appropriate landmarks and body measurement extraction processes for apparel or style‐based measurements is as important as the more commonly derived anthropometric measures based on body landmarks. Landmarks such as side‐seam placement pose unique challenges that must be solved with analysis and reconstruction of style‐based data. The paper provides information on these factors.