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1 – 10 of 585Guangbing Zhou, Jing Luo, Shugong Xu, Shunqing Zhang, Shige Meng and Kui Xiang
Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper…
Abstract
Purpose
Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments.
Design/methodology/approach
Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments.
Findings
The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation.
Originality/value
Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.
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Keywords
Farhad Shamsfakhr, Bahram Sadeghi Bigham and Amirreza Mohammadi
Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s…
Abstract
Purpose
Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments.
Design/methodology/approach
The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment.
Findings
The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima.
Originality/value
By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.
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Keywords
Shengbo Sang, Ruiyong Zhai, Wendong Zhang, Qirui Sun and Zhaoying Zhou
This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems…
Abstract
Purpose
This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems (MEMS) sensor error compensation and the algorithm were improved to realize the localization and altitude accuracy.
Design/methodology/approach
The platform hardware was designed with common low-performance and inexpensive MEMS sensors, and with a barometric altimeter employed to augment altitude measurement. The inertial navigation system (INS) – extended Kalman filter (EKF) – zero-velocity updating (ZUPT) (INS-EKF-ZUPT [IEZ])-extended methods and pedestrian dead reckoning (PDR) (IEZ + PDR) algorithm were modified and improved with altitude determined by acceleration integration height and pressure altitude. The “AND” logic with acceleration and angular rate data were presented to update the stance phases.
Findings
The new platform was tested in real three-dimensional (3D) in-building scenarios, achieved with position errors below 0.5 m for 50-m-long route in corridor and below 0.1 m on stairs. The algorithm is robust enough for both the walking motion and the fast dynamic motion.
Originality/value
The paper presents a new self-developed, integrated platform. The IEZ-extended methods, the modified PDR (IEZ + PDR) algorithm and “AND” logic with acceleration and angular rate data can improve the high localization and altitude accuracy. It is a great support for the increasing 3D location demand in indoor cases for universal application with ordinary sensors.
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Boxin Zhao, Olaf Hellwich, Tianjiang Hu, Dianle Zhou, Yifeng Niu and Lincheng Shen
This study aims to investigate if smartphone sensors can be used in an unmanned aerial vehicle (UAV) localization system. With the development of technology, smartphones have been…
Abstract
Purpose
This study aims to investigate if smartphone sensors can be used in an unmanned aerial vehicle (UAV) localization system. With the development of technology, smartphones have been tentatively used in micro-UAVs due to their lightweight, inexpensiveness and flexibility. In this study, a Samsung Galaxy S3 smartphone is selected as an on-board sensor platform for UAV localization in Global Positioning System (GPS)-denied environments and two main issues are investigated: Are the phone sensors appropriate for UAV localization? If yes, what are the boundary conditions of employing them?
Design/methodology/approach
Efficient accuracy estimation methodologies for the phone sensors are proposed without using any expensive instruments. Using these methods, one can estimate his phone sensors accuracy at any time without special instruments. Then, a visual-inertial odometry scheme is introduced to evaluate the phone sensors-based path estimation performance.
Findings
Boundary conditions of using smartphone in a UAV navigation system are found. Both indoor and outdoor localization experiments are carried out and experimental results validate the effectiveness of the boundary conditions and the corresponding implemented scheme.
Originality/value
With the phone as a payload, UAVs can be further realized in smaller scale at lower cost, which will be used widely in the field of industrial robots.
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Keywords
J. Ahmad, H. Larijani, R. Emmanuel, M. Mannion and A. Javed
Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s…
Abstract
Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes.
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Ala Al‐Fuqaha, Mohammed Elbes and Ammar Rayes
Outdoor localization is an important issue for many applications, such as autonomous mobile robotics and augmented reality. The purpose of this paper is to propose a budgeted…
Abstract
Purpose
Outdoor localization is an important issue for many applications, such as autonomous mobile robotics and augmented reality. The purpose of this paper is to propose a budgeted dynamic exclusion heuristic based on signal phase shifts from multiple base stations.
Design/methodology/approach
The authors also propose an outdoor localization technique based on the particle filter for data fusion and present an overview of a potential target application of the proposed outdoor localization approach for the blind and visually impaired (BVI).
Findings
The combination of multiple sensor data tends to overcome the drawbacks of using one sensor technology in the localization process.
Originality/value
The novelty of the proposed approach stems from its ability to fuse data collected from different sensor technologies to converge to more accurate position estimation.
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Keywords
Chien-Hsing Chen and Ming-Chih Chen
The purpose of this paper is to present a novel position estimation method to accurately locate an object. An accelerometer-based error correction method is also developed to…
Abstract
Purpose
The purpose of this paper is to present a novel position estimation method to accurately locate an object. An accelerometer-based error correction method is also developed to correct the positioning error caused by signal drift of a wireless network. Finally, the method is also utilized to locate cows in a farm for monitoring the action of standing heat.
Design/methodology/approach
The proposed method adopts the received signal strength indicator (RSSI) of a wireless sensor network (WSN) to compute the position of an object. The RSSI signal can be submitted from an endpoint device. A complex environment destabilizes the RSSI value, making the position estimation inaccurate. Therefore, a three-axial accelerometer is adopted to correct the position estimation accuracy. Timer and acceleration are two major factors in computing the error correction value to adjust the position estimate.
Findings
The proposed method is tested on a farm management system for positioning dairy cows accurately. Devices with WSN module and three-axial accelerometer are mounted on the cows to monitor their positions and actions.
Research limitations/implications
If cows in a crowded farm are close to each other, then the position estimation method is unable to position each cow correctly because too many close objects cause interference in the wireless network.
Practical implications
Experimental results demonstrate that the proposed method improves the position accuracy, and monitor the heat action of the cows effectively.
Originality/value
No position estimation method has been utilized to locate cows in a farm, especially for monitoring their actions via WSN and accelerometer. The proposed method adopts an accelerometer to efficiently improve the position error caused from the signal drift of WSN.
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Mohamed Marzouk and Mohamed Zaher
Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing…
Abstract
Purpose
Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.
Design/methodology/approach
Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.
Findings
A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.
Originality/value
The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.
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Shih Chang Hsia, Szu-Hong Wang and Hung-Lieh Chen
This study aims to present a novel technique to localize the human position in a room, to manage people in a specified space.
Abstract
Purpose
This study aims to present a novel technique to localize the human position in a room, to manage people in a specified space.
Design/methodology/approach
In this study, a real-time human sensing detection and smart lighting control was designed within a single silicon core. The chip has been successfully realized within 1.5 mm2 silicon area using TSMC 0.25 um process.
Findings
This chip can read the weak signal of pyroelectric infrared (PIR) sensor to find the position of human body in a dark room and then help control the smart lighting system for an intelligent surveillance system.
Originality/value
This chip presented the retriggering delay control to expand the LED lighting time infinitely to avoid lighting-off suddenly while users stay on a space. This function is very useful in a practical intelligent surveillance system that is mainly based on human detection to better reduce power dissipation and memory space.
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