An Optimized Algorithm for Indoor Localization and Concepts for Anonymous Dynamic Obstacle Detection

\nIn the safety area of industrial production, robots are required to have the ability of quickly detecting the dynamic obstacles which appear in the working space of robots when robots move along a predefined route. In order to solve the problem of dynamic obstacles detection more effectively, the paper proposes a quick detection method based on background compensation for dynamic obstacles in robot movement space, which makes extraction of feature points on dynamic obstacles in robot movement space, establishes the background model of optical flow velocity field between adjacent frames and makes background compensation for images with the use of block-matching algorithm in order to realize quick and accurate dynamic obstacles detection in dynamic environment. The experiment results show that the obstacles detection method can effectively eliminate the background movement caused by the movement of camera at the end of robot and can extract a more complete target object. Moreover, it also has good robustness.\n

It may be very difficult to receive the signals from satellite positioning systems due to the existing obstacles in indoor environment. Arising from the popularity of smart phones, Wi-Fi based indoor positioning technology has the advantages with convenient deployment and low hardware cost. In this study, we focused on indoor positioning using Wi-Fi fingerprint data that were collected in shopping malls. Due to the volatility of Wi-Fi signals and the high-dimensional sparseness of fingerprint data, we proposed a feature extraction algorithm, called joint multi-task stacked denoising auto-encoder (JMT-SDAE), aiming at reducing the dimensionality of the original fingerprint data and improving the indoor positioning performance in shopping malls. Furthermore, the features extracted by JMT-SDAE and gradient boosting decision tree (GBDT) were merged to construct a hybrid model, named as JMT-SDAE+GBDT. The experimental results based on 13 location datasets showed that the proposed feature fusion model had better positioning accuracy when compared with other existing positioners, and thus confirmed the effectiveness of our proposed feature extraction algorithm through multi-task learning.

Presents a new framework for detection of dynamic obstacles in the unstructured outdoor road environment by integrating binocular color image sequences. In the authors' system, color image segmentation, stereo obstacle detection, visual egomotion estimation, and moving object analysis, are all built-in task-oriented modules and hence are efficient and robust. Most of these functions can be performed in real-time. They are activated and integrated adaptively in the manner of neural parallel distributed processing (PDP). Experimental results are given to validate the authors' philosophy of so-called purposive vision, retinal mapping, adaptive integration and parallel distributed processing. >

Aimed at the detection of static and dynamic obstacles in environmental mapping of mobile robot, an unsupervised clustering algorithm is presented to realize feature extraction of obstacles based on the analysis of ranging data obtained from 2D laser scanner. Considering the unknown clustering number in advance, the validation index function is introduced into the self-learning mechanism to determine the accurate clustering number automatically. At the same time, fuzzy logic is integrated into incremental data association of obstacle features to make the static or dynamic obstacles classification decision to reduce the uncertain influence. Using our office as the operating environment to implement the experiment of feature extraction and obstacles classification, the results verify the effectiveness of this approach.

Indoor air quality (IAQ) is a crucial determinant of health, which is of particular concern for low-income Black children, who are disproportionately exposed to asthma triggers in their indoor housing environment and have higher rates of asthma and poorer outcomes from the disease, indicating an environmental justice issue. However, guidelines for IAQ data collection vary between organizations, making assessments and comparisons difficult. This study consolidates recommended practices into one cohesive set of guidelines for monitoring IAQ. It outlines a structured approach for defining IAQ sampling objectives, identifying the most appropriate low-cost air sensors, determining optimal sensor placement in homes, developing data collection and analysis, and presenting and interpreting results. With indoor PM2.5 as a case study, the guidelines were used to determine sampling duration, analysis, and quality assurance in five households with pediatric asthma patients. The metrics illustrate PM2.5 concentration by the 90th percentile (with average concentration level from 4 to 109 μg/m3), daily average minutes of PM2.5 concentration exceeding recommended exposure limits of 0–134 min, daily average PM2.5 concentration of 1.8–82 μg/m3, and significant temporal variation across households. This study highlights the significance of employing low-cost air sensors to provide real-time notifications of IAQ, along with simplified monitoring guidelines for researchers, healthcare providers, and parents working to improve IAQ for households with asthmatic children, potentially reducing the frequency and severity of asthma attacks. The low-cost sensors and resulting metrics can direct interventions effectively and provide a basis for determining the efficacy of environment-based air quality interventions. Implications: People spend much of their time indoors, especially at home, making indoor air quality (IAQ) monitoring crucial for guiding actions that reduce asthma exacerbations, particularly among low-income households. Although low-cost air sensors are being widely used for real-time IAQ monitoring, most existing guidelines focus on occupational settings rather than residential environments, making implementation difficult. This study develops comprehensive and unified guidelines for deploying low-cost sensors for real-time IAQ monitoring in residential settings. A case study utilizing the developed guideline in this study was applied to demonstrate and evaluate PM2.5 concentration levels in some low-income residential homes in Southeastern Louisiana.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

Due to the limitation of Global Navigation Satellite System (GNSS), it cannot work properly without direct line of sight to GNSS satellites such as in urban and indoor environments. Visual processing such as Visual Odometry (VO) and Structure-From-Motion (SFM) is usually used for the indoor navigation or localization. SFM was introduced in 1990 and had been successfully used for reconstruction of unordered images, and in the process of reconstruction, the SFM would estimate the position of the camera for each image at the same time. This paper introduces three most common SFM tools and suggests a positioning method for small UAVs equipped with vision cameras in indoor environment. Each stage of the SFM algorithms is studied to analysis the impact on the final positioning accuracy. The output from different feature detection, feature matching, initial pair setting, and the reconstructions are compared with an indoor reference localization system based on infrared cameras. The objective of this paper is to suggest a positioning and navigation method for small UAVs equipped with vision cameras in indoor environment. In this research, we will study the impact of different stages of SFM algorithm on the final positioning accuracy. Several objects such as chairs or boxes would be placed randomly in a lab room to build a feature-based environment without any special landmarks. In our experiment, an indoor localization system based on infrared cameras will be used as the reference navigation result to evaluate the final performance of the suggested algorithm.

Ferketich et al. studied demographic factors associated with the passage of clean indoor air ordinances in Appalachian communities.1 Based on reported lack of progress at the local level despite strong public support for clean indoor air laws, they recommended that efforts be focused on the state level instead. This conclusion goes beyond the data that they present and does not consider the power of the tobacco industry in state-level politics.2–6 Additionally, recent experience in South Carolina,7 a state Ferketich et al. included in their research, shows that strong progress on local clean indoor air ordinances is possible even in an Appalachian, tobacco-growing state. South Carolina's weak state clean indoor air ordinances passed in 1996 with an assumed preemption clause pushed by cigarette manufacturer lobbyists and hospitality industry allies that halted clean indoor ordinance progress for a decade. However, between May 2006 and January 2008, local advocates, supported by national tobacco control technical assistance and funding, challenged this presumed preemption by passing clean indoor air ordinances in 12 localities, two of which were sued under claims that state preemption did not allow local clean air ordinances. In March 2008, the state Supreme Court ruled that local clean indoor air ordinances were not preempted; since then, advocates have passed 21 more local clean indoor air ordinances. The passage of these local clean indoor air ordinances as of May 2010 has been recognized as the highest number of strong local ordinances passed in any US state for two years in a row.8 Cigarette manufacturing interests responded with an effort to pass explicit state preemption. During the 2007 and 2008 legislative session, 11 neutral to strong clean indoor air bills were introduced; three were co-opted to include weak clean indoor air provisions and preemptive language as a result of tobacco manufacturer lobbying. Tobacco control advocates stopped all weak bills with preemption and convinced legislators to delay state laws, which could become vehicles for preemption.7 The trajectory of South Carolina clean indoor air ordinance progress provides a strong counterpoint to Ferketich et al.'s conclusion that there is a lack of motivation among tobacco control advocates at the local level in tobacco-growing states and that clean indoor air ordinance efforts should focus at the state level. Developing clean indoor air laws at the state level without strong local support provides an opportunity for cigarette manufacturers to preempt more comprehensive local activity, where tobacco manufacturers have less sway.3,9 In contrast, developing the capacity of local advocates can result in a strong smoke-free movement through local smoke-free ordinance adoption.

<p>The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.</p>

Indoor localization has gained significant attention recently due to the growing demand for location-aware applications within indoor environments. Among various techniques, Wi-Fi fingerprinting combined with the Internet of Things (IoT) has emerged as a promising solution for accurate and cost-effective indoor localization. This paper surveys the existing research related to indoor localization using Wi-Fi fingerprinting with the IoT, aiming to provide a comprehensive understanding of the advancements, challenges, and potential applications in this field. The paper begins by introducing the fundamental concepts of indoor localization and the role of Wi-Fi fingerprinting in achieving accurate position estimation. In addition, this paper focuses on the contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in improving localization accuracy, robustness, and scalability. The reviewed papers have been examined from various aspects, including system architecture, deployment strategies, fingerprint creation techniques, and localization algorithms, with a discussion about the advantages and limitations of each approach. In addition to discussing the state-of-the-art techniques, this paper identifies research gaps and open challenges in indoor localization using Wi-Fi fingerprinting with the IoT. The findings presented in this paper can guide future research efforts, leading to the development of intelligent and context-aware IoT applications within indoor environments.

Location-based service in the indoor environment is playing a crucial role in different application scenarios. The introduction of technologies, such as ultradense network and massive multiple-input multiple-output enables fifth-generation (5G) cellular signals, as a new generation of cellular network signals, to show unique advantages in indoor positioning. This article describes 5G reference signal structures that can be used for navigation. A high-precision time-of-arrival estimation method based on 5G downlink signal is proposed that can be realized by edge computing. A software-defined receiver (SDR) based on machine learning to extract navigation observations from 5G signals is then developed. In simulation, the error sources of SDR in additive white gaussian noise channel and multipath channel were analyzed, and the possible ranging accuracy achieved by 5G signals in the developed SDR was evaluated. In field experiments, commercial 5G signals deployed by operators were collected, and the performance of SDR in practical applications was evaluated. The feasibility in practical applications of the proposed SDR is demonstrated, and high pseudorange measurement accuracy can be achieved.

Since the well-established navigation systems such as GPS are ineffective in indoor environments, research into developing novel indoor positioning technologies has emerged in recent years. While several technologies are being investigated, a practical and reliable indoor positioning system is yet to emerge. Indoor positioning using light signals holds a great potential to provide a reliable solution to the indoor positioning problem. This paper presents a comprehensive review of the available literature on light-based indoor positioning systems. The paper establishes a classification based on the design of various light-based indoor positioning (LIP) systems, and discusses their key components including hardware, software and algorithms. The limitations and challenges of the LIP systems are discussed in details, and current published solutions are presented. The review also provides a comparison between LIP systems and other optical positioning systems, and identifies the associated research gaps. Finally, based on the identified gaps, the future research directions in both LIP systems and other optical positioning systems are presented.

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers, because of their potential use in several fields such as collaborative computing and communications. In this paper, we present the implementation of a testbed for WMNs using Raspbian and OpenWRT OSs. We analyze the performance of Optimized Link State Routing (OLSR) protocol in an indoor scenario. For evaluation we considered throughput, Packet Delivery Ratio (PDR), delay, jitter and hop count metrics. The experimental results show that the testbed mounted in OpenWRT has better results than Raspbian.

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

Indoor Positioning System (IPS) has always been a focused area of research due to an increasing demand for IPS in a variety of civilian and defence applications. Bluetooth low energy beacons are best suited for IPS owing to their compatibility with mobile devices such as smart phones. In this paper, a standard bluetooth module is configured to work as a Bluetooth-Beacon (BT-Beacons) and an IPS is implemented using the RSSI values of signals from the BT-Beacons. The position of user in indoors is computed using the principle of trilateration. The proposed system achieved good positioning accuracy and is economical. Further, the proposed system can be improved to assist localization and navigation in indoor environment such as huge buildings.