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Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.

This article explores localisation in humanitarian settings as an example of sustainability and inner development. Through a case study from Syria, we discuss how localisation and remote management can lead to the mutual flourishing of individuals, communities and planetary health in the most challenging settings. Through localisation, we can rethink and reframe humanitarianism and integrate sustainability and personal development. Learning from these collaborations that highlight the importance of trust and interpersonal relations, the humanitarian and global health communities can reflect on how local individuals and communities can be further supported in their personal development and the sustainability of interventions that promote planetary health.

Background and Objectives: This review paper highlights the key alternatives to the blue dye/radioisotope method of sentinel lymph node biopsy (SLNB). It analyses the research available on these alternative methods and their outcomes compared to the traditional techniques. Materials and Methods: This review focused on fifteen articles, of which five used indocyanine green (ICG) as a tracer, four used magnetic tracers, one used one-step nucleic acid amplification (OSNA) and Metasin (quantitative reverse transcriptase-polymerase chain reaction), one used the photosensitiser talaporfin sodium, one used sulphur hexafluoride gas microbubbles, one used CT-guided lymphography and two focused on general SLNB technique reviews. Results: Of the 15 papers analysed, the sentinel node detection rates were 69–100% for indocyanine green, 91.67–100% for magnetic tracers, 81% for talaporfin sodium, 9.3–55.2% for sulphur hexafluoride gas microbubbles, 90.5% for CTLG and 82.7–100% for one-step nucleic acid amplification. Conclusions: Indocyanine green fluorescence (ICG) and magnetic tracers have been proven non-inferior to traditional blue dye and isotope regarding SLNB localisation. Further studies are needed to investigate the use of these techniques in conjunction with each other and the possible use of language learning models. Dedicated studies are required to assess cost efficacy and longer-term outcomes.

Over the past two decades, in response to a growing awareness of the impacts of humanitarian crises on mental health and psychosocial well-being, leading UN agencies and international aid organisations have developed a comprehensive framework for Mental Health and Psychosocial Support (MHPSS). In more recent years, aid workers have further begun to consider religious life as a central factor in mental health and psychosocial well-being, viewing “faith” as an important, but often neglected, component of empowering and “locally appropriate” MHPSS. However, the attempt to deliver “faith-sensitive” MHPSS across the highly pluralistic settings of international humanitarian intervention has entailed protracted ethical and practical challenges. In this article, we argue that these challenges may be usefully understood in terms of three areas of concern: the lack of evidence on effective interventions; the risk of reproducing problematic power dynamics between MHPSS providers and receivers; and the challenge of articulating a cross-culturally relevant paradigm of “faith-sensitivity” comprehensible across a wide range of religiously diverse settings. This article contributes to these challenges by drawing on the field of professional spiritual care to suggest areas of potential contribution and interdisciplinary dialogue.

This paper focuses on the Hellenistic Middle East, especially the age of Ptolemaic Alexandrian and Syrian Seleucid influence. It investigates and clarifies some of the Hellenistic-age historical and archaeological material culture within the Hellenisation and globalisation conceptions. Furthermore, it suggests that by reviewing the context of the local socio-cultural identities in the Hellenistic Oikoumene, mainly based on the lingua franca about local identity and how the local identity was expressed on coinage during Hellenistic times, many related insights issues can be revealed. In addition, it also attempts to discuss and reveal aspects of the cultural sharing achievements in Hellenistic art, architecture, and urban built environment planning. Finally, how did Eastern Hellenistic cities manage to benefit from the process of Hellenistic globalisation and localisation/globalisation while minimising identity risks? The focus is on the transnational socio-cultural and economic area of Ptolemaic Alexandria, the centre of the post-Classical Greek world, and the Syrian Seleucid influence. As an investment, mass migration and the transfer of goods, culture, and ideas increasingly transformed these Middle Eastern cities and shaped their translocal culture conception, local socio-cultural identities, cultural sharing, art and architecture edifice forms, and spatial patterns in the Hellenistic period. One of the main contributions and significance of this study is to continue the dialogue of how non-Greek influence in Hellenistic times impacted an area that has been traditionally seen as unaffected or minimally affected by years under foreign rule. This also sheds new light on some Greco-Macedonian topics not sufficiently debated in the Oikoumene discussion dialogue. These two aspects would furthermore contribute to better understanding and accepting the neglected role of the contribution of non-Greek culture to Greek achievements, as well as how the local non-Greek customs of the indigenous peoples of the Ptolemy and Seleucid kingdoms would affect how they assimilated Greco-Macedonian practices, and how the vision of Alexander the Great and Hellenisation worked in the different territories of these two kingdoms.

In response to the construction sector’s contribution to the climate crisis and exacerbation of social inequalities, we explore sustainable alternatives in building construction, informed by the illustrative case study of the Polycare construction system. First, through a Life-Cycle Assessment (LCA) method, we show that the ecological footprint of circularity-oriented buildings based on polymer concrete is significantly lower than that of conventional cement concrete buildings. Despite the drawbacks of polymer concrete, its high-performance properties and the possibility to integrate secondary materials in its recipe can result in a reduced carbon footprint. When coupled with design-embedded modularity that facilitates circular processes (e.g., the disassembly and reuse of building components), buildings similar to those in the case study demonstrate potential for transitioning towards comprehensive sustainable building practices. Further, we discuss how this sustainability potential could be enhanced, drawing from interviews with Polycare’s stakeholders and key literature findings. In this direction, we provide a set of proposals anchored in the argument that threefold “circularity, localisation, and openness” is vital for sustainable and affordable alternatives, with openness being a crucial element for fostering innovation, adaptability, and scalability in building processes.

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there is currently no effective method for determining a single ground truth value or landmark from a set of annotations in regression problems. We propose a novel method for deriving ground truth labels in regression problems that considers the performance and precision of individual annotators when identifying each label separately. In contrast to the commonly accepted method of computing the global mean, our method does not assume each annotator to be equally capable of completing the specified task, but rather ensures that higher-performing annotators have a greater contribution to the final result. The ground truth selection method described within this paper provides a means of improving the quality of input data for machine learning model development by removing lower-quality labels. In this study, we objectively demonstrate the improved performance by applying the method to a simulated dataset where a canonical ground truth position can be known, as well as to a sample of data collected from crowd-sourced labels.

Background and Objectives: Wide local excision is a common procedure in the treatment of breast cancer. Wire-guided localisation (WGL) has been the gold standard for many years; however, several issues have been identified with this technique, and therefore, wire-free techniques have been developed. This scoping review synthesises the available literature comparing wire-guided localisation with the wire-free techniques used in breast-conserving cancer surgery. Materials and Methods: Multiple databases including Pubmed and MEDLINE were used to search articles between 1 January 2000 and 31 December 2022. Terms included “breast neoplasms”, “margins of excision”, and “reoperation”. In total, 34/256 papers were selected for review. Comparisons were made between positive margins and re-excision rates of WGL with wire-free techniques including SAVI SCOUT, Magseed, ROLL, and RSL. Pooled p-values were calculated using chi-square testing to determine statistical significance. Results: Pooled analysis demonstrated statistically significant reductions in positive margins and re-excision rates when SAVI SCOUT, RSL, and ROLL were compared with WGL. When SAVI SCOUT was compared to WGL, there were fewer re-excisions {(8.6% vs. 18.8%; p = 0.0001) and positive margins (10.6% vs. 15.0%; p = 0.0105)}, respectively. This was also the case in the ROLL and RSL groups. When compared to WGL; lower re-excision rates and positive margins were noted {(12.6% vs. 20.8%; p = 0.0007), (17.0% vs. 22.9%; p = 0.0268)} for ROLL and for RSL, respectively {(6.8% vs. 14.9%),(12.36% vs. 21.4%) (p = 0.0001)}. Magseed localisation demonstrated lower rates of re-excision than WGL (13.44% vs. 15.42%; p = 0.0534), but the results were not statistically significant. Conclusions: SAVI SCOUT, Magseed, ROLL, and RSL techniques were reviewed. Pooled analysis indicates wire-free techniques, specifically SAVI SCOUT, ROLL, and RSL, provide statistically significant reductions in re-excision rates and positive margin rates compared to WGL. However, additional studies and systematic analysis are required to ascertain superiority between techniques.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic remapping, updating the map is recommended. However, in the case of incorrect robot pose estimation, updating the map can lead to errors that prevent the robot’s localisation and jeopardise map accuracy. In this paper, we propose a safe Lidar-based occupancy grid map-updating algorithm for dynamic environments, taking into account uncertainties in the estimation of the robot’s pose. The proposed approach allows for robust long-term operations, as it can recover the robot’s pose, even when it gets lost, to continue the map update process, providing a coherent map. Moreover, the approach is also robust to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland.

Offshore support structures and mooring systems are predominantly subject to corrosion and fatigue. These structures are typically covered with marine growth of various types. Conventional inspection methods for assessment of the structural integrity require access to the cleaned surface of these structures; however, the cleaning process is highly undesirable from the technical, economical, and environmental points of view. This paper highlights research on feasibility assessment of detection and localization of corrosion damage under marine growth using acoustic emission (AE). Experiments were conducted on two carbon steel plates, one baseline sample and one covered with artificially fabricated marine growth. The results of accelerated corrosion experiments suggest that corrosion-induced ultrasound signals can be detected with satisfactory signal-to-noise ratio using non-contact AE sensors. Ultrasound waves passing through marine growth showed around 12 dB drop in amplitude when compared to the base plate. A localization algorithm for corrosion induced-ultrasound signals was successfully implemented.

The article presents the most commonly used multi-criteria analysis methods for choosing the optimal location for future wind parks. The article makes a comparison of the criteria and restrictions of localisation and an overview of the main legal constraints and prospects in the development of renewable energy sources (RES). Financial assistance from the EU to accelerate the achievement of the required indicators was described. Moreover, restrictions considering environmental, social and noise factors that affect the life of the local population and the perception of the landscape visually are important. Additionally, it includes an option for developing wind energy in the absence of the necessary space for construction. In a new approach for the location of the wind farm, to the investors and another researcher related to the topic of wind turbine foundations, we indicate the most important aspects of wind energy control that should be taken into account in wind farm location proceedings.

The recent advancements in Information and Communication Technology (ICT) as well as increasing demand for vehicular safety has led to significant progressions in Autonomous Vehicle (AV) technology. Perception and Localisation are major operations that determine the success of AV development and usage. Therefore, significant research has been carried out to provide AVs with the capabilities to not only sense and understand their surroundings efficiently, but also provide detailed information of the environment in the form of 3D maps. Visual Simultaneous Localisation and Mapping (V-SLAM) has been utilised to enable a vehicle understand its surroundings, map the environment, and identify its position within the area. This paper presents a detailed review of V-SLAM techniques implemented for AV perception and localisation. An overview of SLAM techniques is presented. In addition, an in-depth review is conducted to highlight various V-SLAM schemes, their strengths, and limitations. Challenges associated with V-SLAM deployment and future research directions are also provided in this paper.

Recent deep learning frameworks draw strong research interest in application of ego-motion estimation as they demonstrate a superior result compared to geometric approaches. However, due to the lack of multimodal datasets, most of these studies primarily focused on single-sensor-based estimation. To overcome this challenge, we collect a unique multimodal dataset named LboroAV2 using multiple sensors, including camera, light detecting and ranging (LiDAR), ultrasound, e-compass and rotary encoder. We also propose an end-to-end deep learning architecture for fusion of RGB images and LiDAR laser scan data for odometry application. The proposed method contains a convolutional encoder, a compressed representation and a recurrent neural network. Besides feature extraction and outlier rejection, the convolutional encoder produces a compressed representation, which is used to visualise the network’s learning process and to pass useful sequential information. The recurrent neural network uses this compressed sequential data to learn the relationship between consecutive time steps. We use the Loughborough autonomous vehicle (LboroAV2) and the Karlsruhe Institute of Technology and Toyota Institute (KITTI) Visual Odometry (VO) datasets to experiment and evaluate our results. In addition to visualising the network’s learning process, our approach provides superior results compared to other similar methods. The code for the proposed architecture is released in GitHub and accessible publicly.

Molybdate uptake and molybdenum cofactor (Moco) biosynthesis were investigated in detail in the last few decades. The present study critically reviews our present knowledge about eukaryotic molybdate transporters (MOT) and focuses on the model plant Arabidopsis thaliana, complementing it with new experiments, filling missing gaps, and clarifying contradictory results in the literature. Two molybdate transporters, MOT1.1 and MOT1.2, are known in Arabidopsis, but their importance for sufficient molybdate supply to Moco biosynthesis remains unclear. For a better understanding of their physiological functions in molybdate homeostasis, we studied the impact of mot1.1 and mot1.2 knock-out mutants, including a double knock-out on molybdate uptake and Moco-dependent enzyme activity, MOT localisation, and protein–protein interactions. The outcome illustrates different physiological roles for Moco biosynthesis: MOT1.1 is plasma membrane located and its function lies in the efficient absorption of molybdate from soil and its distribution throughout the plant. However, MOT1.1 is not involved in leaf cell imports of molybdate and has no interaction with proteins of the Moco biosynthesis complex. In contrast, the tonoplast-localised transporter MOT1.2 exports molybdate stored in the vacuole and makes it available for re-localisation during senescence. It also supplies the Moco biosynthesis complex with molybdate by direct interaction with molybdenum insertase Cnx1 for controlled and safe sequestering.

Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle’s position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.

The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.

In this paper, we discuss two problems concerning scattering and localisation of flexural waves in structured elastic plates. Firstly, we compare the scattering amplitudes of waves in a thin plate, generated by a point source, due to a single mass and to a large number of smaller masses, having the same equivalent mass and located around a circle. We show that in the second case, the scattering can be reduced, in particular in the medium- and high-frequency regimes. Secondly, we develop a homogenised model for a double-ring cluster of spring-mass resonators, connected to an elastic thin plate. We determine the conditions for which the plate exhibits vibration modes trapped between the two rings. Further, we show that the frequencies of the localised modes can be tuned by varying the geometry of the two rings and the characteristics of the resonators. The analytical results are corroborated by numerical simulations performed with independent finite element models.

Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

Dundee City has been successful in installing green infrastructure for charging electric vehicles (EVs). This intervention matches the Sustainable Development Goals (SDGs) of affordable clean energy (SDG 7), sustainable cities and communities (SDG 11) and climate action (SDG 13) of the United Nations General Assembly Agenda 2030 (Transforming our World: the 2030 Agenda for Sustainable Development). Local authorities can align interventions with SDGs according to needs. The purpose of this paper is to consider whether Dundee’s EV strategy represents the most viable and equitable intervention that could be adopted given the city’s context. We adopt a positive review and value argumentation approach to determine the extent to which the strategy satisfies the criteria of “level of urgency”, “systemic impact” and “policy gaps”, which have been employed in the extant literature as the basis for a multi-criteria analysis (MCA). We eclectically review elements of the strategy against the city’s peculiar physical and socio-economic environment, as well as argue their fit against these criteria. We interpret these criteria based on the complementarity and benefits of the strategy from the lenses of SDG 7, SDG 11 and SDG 13. Additionally, we consider the alignment of the EV strategy with the other SDGs. The criteria also allow us to evaluate the strategy based on the localisation principles of equity, acceptability and affordability of the intervention. Our review shows that the EV strategy represents a sustainable and community life-enhancing intervention that aligns with some key SDGs. However, the outcome raises concerns about the equitability of the strategy. Smaller, similar or bigger cities could utilise this approach. However, we recommend the evaluation of local priorities to improve alignment with the SDGs and the provision of clear justifications for selecting an intervention from a range of responses.

Radiation mapping, through the detection of ionising gamma-ray emissions, is an important technique used across the nuclear industry to characterise environments over a range of length scales. In complex scenarios, the precise localisation and activity of radiological sources becomes difficult to determine due to the inability to directly image gamma photon emissions. This is a result of the potentially unknown number of sources combined with uncertainties associated with the source-detector separation—causing an apparent ‘blurring’ of the as-detected radiation field relative to the true distribution. Accurate delimitation of distinct sources is important for decommissioning, waste processing, and homeland security. Therefore, methods for estimating the precise, ‘true’ solution from radiation mapping measurements are required. Herein is presented a computational method of enhanced radiological source localisation from scanning survey measurements conducted with a robotic arm. The procedure uses an experimentally derived Detector Response Function (DRF) to perform a randomised-Kaczmarz deconvolution from robotically acquired radiation field measurements. The performance of the process is assessed on radiation maps obtained from a series of emulated waste processing scenarios. The results demonstrate a Projective Linear Reconstruction (PLR) algorithm can successfully locate a series of point sources to within 2 cm of the true locations, corresponding to resolution enhancements of between 5× and 10×.

This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions.

The inspection of aquatic environments is a challenging activity, which is made more difficult if the environment is complex or confined, such as those that are found in nuclear storage facilities and accident sites, marinas and boatyards, liquid storage tanks, or flooded tunnels and sewers. Human inspections of these environments are often dangerous or infeasible, so remote inspection using unmanned underwater vehicles (UUVs) is used. Due to access restrictions and environmental limitations, such as low illumination levels, turbidity, and a lack of salient features, traditional localisation systems that have been developed for use in large bodies of water cannot be used. This means that UUV capabilities are severely restricted to manually controlled low-quality visual inspections, generating non-geospatially located data. The localisation of UUVs in these environments would enable the autonomous behaviour and the development of accurate maps. This article presents a review of the state-of-the-art in localisation technologies for these environments and identifies areas of future research to overcome the challenges posed.

In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms state-of-the-art approches.

This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, refractions and diffractions undergone by the ultra-high frequency (UHF) signal within the equipment tank. The proposed method uses computational simulations of the electromagnetic waves generated by PD, and obtains the time delay of the signal between each point in the 3D space and the UHF sensors. The calculated signals can be compared with the signals measured in the field, so that the position of the PD source can be located based on the best correlation between the simulated propagation delay and the measured data. The equations used in the proposed method are defined as a 3D optimisation problem, so that the binary particle swarm optimisation algorithm can be used. To test and demonstrate the proposed algorithm, computational simulations were performed. The solutions were sufficient to identify not only the occurrence of defects, but also the winding and the region (top, centre or base) in which the defect occurred. In all cases, an accuracy of greater than 15 cm was obtained for the location, in a 180 MVA three-phase transformer.

Public transportation (PT) systems suffer from disutility compared to private transportation due to the inability to provide passengers with a door-to-door service, referred to as the first/last mile problem. Personal mobility devices (PMDs) are thought to improve PT service quality by closing this first/last mile gap. However, current PMDs are generally driven manually by the rider and require a learning phase for safe vehicle operation. Additionally, most PMDs require a standing riding position and are not easily accessible to elderly people or persons with disabilities. In this paper, the concept of an autonomously operating mobility device is introduced. The visionary concept is designed as an on-demand transportation service which transports people for short to medium distances and increases the accessibility to public transport. The device is envisioned to be operated as a larger fleet and does not belong to an individual person. The vehicle features an electric powertrain and a one-axle self-balancing design with a small footprint. It provides one seat for a passenger and a tilt mechanism that is designed to improve the ride comfort and safety at horizontal curves. An affordable 3D-camera system is used for autonomous localization and navigation. For the evaluation and demonstration of the concept, a functional prototype is implemented.

This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better.

The latest developments in the field of digital humanities have increasingly enabled the construction of large data sets which can be easily accessed and used. These data sets often contain indirect spatial information, such as historical addresses. Historical geocoding is the process of transforming indirect spatial information into direct locations which can be placed on a map, thus allowing for spatial analysis and cross-referencing. There are many geocoders that work efficiently for current addresses. However, these do not tackle temporal information, and usually follow a strict hierarchy (country, city, street, house number, etc.) which is difficult—if not impossible—to use with historical data. Historical data is filled with uncertainty (pertaining to temporal, textual, and positional accuracy, as well as to the reliability of historical sources) which can neither be ignored nor entirely resolved. Our open source, open data, and extensible solution for geocoding is based on extracting a large number of simple gazetteers composed of geohistorical objects, from historical maps. Geocoding a historical address becomes the process of finding one or several geohistorical objects in the gazetteers which best match the historical address searched by the user. The matching criteria are customisable, weighted, and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). Since our goal is to facilitate historical work, we also put forward web-based user interfaces which help geocode (one address or batch mode) and display results over current or historical maps. Geocoded results can then be checked and edited collaboratively (no source is modified). The system was tested on the city of Paris, France, for the 19th and 20th centuries. It showed high response rates and worked quickly enough to be used interactively.

Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisation Extended Kalman Filter which fuses Pozyx Ultra-wideband range measurements with odometry to obtain an optimal position estimation, all whilst using the path generated from the remote tracking feature of the Robotic Total Station as a ground truth metric. Experiments show that the proposed method yields an improved positional estimation compared to the Pozyx systems’ native firmware algorithm as well as producing a smoother trajectory.

Manual therapy (MT) techniques typically incorporate localised touch on the skin with the application of specific kinetic forces. The contribution of localised touch to the effectiveness of MT techniques has not been evaluated. This study investigated the immediate effects of MT versus localisation training (LT) on pain intensity and range of movement (ROM) for neck pain. In this single-blind randomised controlled trial thirty eligible neck pain volunteers (23 females and 7 males), aged 28.63 ± 12.49 years, were randomly allocated to MT or to a motionless (LT) group. A single three-minute treatment session was delivered to each group's cervico-thoracic area. The LT involved tactile sensory stimulation applied randomly to one out of a nine-block grid. Subjects were asked to identify the number of the square being touched, reflecting a different location on the region of skin. MT involved three-minute anteroposterior (AP) glides and sustained natural apophyseal glides (SNAG) techniques. Pre- and post-intervention pain intensity were assessed using a pressure pain threshold (PPT) algometer and the numeric pain rating scale (NPRS). Neck ROM was recorded with a bubble inclinometer. Improvements in ROM and self-reported pain were recorded in both groups ( p < 0.001) without differences in NPRS, ROM or PPT scores between groups ( p > 0.05). Tactile sensory training (localisation) was as effective as MT in reducing neck pain, suggesting a component of MT's analgesic effect to be related with the element of localised touch rather than the forces induced during passive movements.