Robust Positioning Performance in Indoor Environments

Increasingly, safety and liability critical applications require GNSS-like positioning metrics in environments where GNSS cannot work. Indoor navigation for the vision impaired and other mobility restricted individuals, emergency responders and asset tracking in buildings demand levels of positioning accuracy and integrity that cannot be satisfied by current indoor positioning technologies and techniques. This paper presents the challenges facing positioning technologies for indoor positioning and presents innovative algorithms and approaches that aim to enhance performance in these difficult environments. The overall aim is to achieve GNSS-like performance in terms of autonomous, global, infrastructure free, portable and cost efficient. Preliminary results from a real-world experimental campaign conducted as part of the joint FIG Working Group 5.5 and IAG Sub-commission 4.1 on multi-sensor systems, demonstrate performance improvements based on differential Wi-Fi (DWi-Fi) and cooperative positioning techniques. The techniques, experimental schema and initial results will be fully documented in this paper

Experimental Evaluation of a UWB-Based Cooperative Positioning System for Pedestrians in GNSS-Denied Environment

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included

A Benchmarking Measurement Campaign to Support Ubiquitous Localization in GNSS Denied and Indoor Environments

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Cooperative localisation of unmanned aerial vehicles using low cost sensors

© 2017 Dr. Salil GoelCompleted under a Cotutelle arrangement between the University of Melbourne and Melbourne India Postgraduate Academy (MIPA)The reliance on location and location based services in everyday life is undergoing tremendous growth as society progresses towards an increasingly connected world. Location awareness plays an important role in many applications such as navigation, mapping, exploration, emergency response, surveillance, search and rescue, etc. and forms an integral component of almost all modern technologies some of which include connected vehicles, Intelligent Transport Systems, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) and smart cities. UAVs are increasingly being used in the above mentioned applications as well as in other domains such as agriculture and insurance. The use of UAV in any applications is contingent to precise and continuous localisation of the UAV platform. Till today, GNSS has been the primary source for achieving a precise localisation solution. However, the performance of GNSS is subject to the availability of clear outdoor environments and degrades substantially in occluded environments such as urban canyons and forests. A new paradigm of positioning is emerging that utilises cooperation and information sharing among UAVs as well as existing infrastructure and other platforms (or nodes) for localisation and is termed as ‘Cooperative Localisation’. Information sharing among nodes can help overcome some of the challenges including precise and continuous positioning in challenging environments such as urban environments, forests etc. Further, cooperative localisation may help in improving the positioning accuracy and is required for the deployment of UAV swarms in various applications. Although the advantages of cooperative localisation have been apparent, the performance of a cooperative localisation system of a swarm of UAVs, impact of various components on its performance and its advantages and limitations have not been evaluated in real world conditions. This research develops the mathematical framework and a prototype of a cooperative localisation system for a swarm of UAVs using GNSS, inertial and Ultra Wide Band sensors and performs an extensive performance analysis using multiple real world experiments. Notable developments achieved in this research include design and development of a new cooperative localisation prototype for a UAV swarm network, a general framework for cooperative localisation in heterogeneous and homogeneous cooperative networks using centralised architecture, development and evaluation of a new distributed EKF based estimation algorithm that is less computationally expensive than existing algorithms. Following a critical analysis of the existing literature to identify the research gaps, the details of the developed prototype are presented. Further, a performance analysis of the on-board sensors is performed to establish the performance parameters that are needed for information fusion. This is followed by the development of a general mathematical framework for cooperative localisation in centralised and distributed architectures for both heterogeneous and homogeneous networks. This framework is used to perform a sensitivity analysis, to establish performance bounds and study the impact of various factors on the overall performance of the proposed system. This is followed by the experimental evaluation of the developed prototype in real world environments. From these experiments, it is demonstrated for the first time that a cooperative UAV localisation system based on low cost sensors is capable of achieving localisation accuracy of the order of ∼ 3 − 5 m in partially GNSS denied environments, when communication among UAVs is consistent. Through these experiments, the effect of the quality of communication on the localisation accuracy of UAVs is demonstrated and it is found that a consistent communication helps maintain the localisation accuracy to about 3 − 4 m in partially GNSS denied environments. Furthermore, it is demonstrated using experiments that cooperative localisation can help improve the positioning accuracy even in GNSS available environments. It is found from theoretical analysis that the effect of loss of GNSS measurements on the localisation performance of a node in a swarm can be minimised by altering the network geometry. A complete analysis of the limitations of the developed system and some suggestions for the future work are also presented. Through this research, the performance of a cooperative UAV network is evaluated in real world environments and its limitations are highlighted

Navigation in Urban Environments

This chapter provides an overview of technologies and methodologies for navigation in urban environments. It covers a range of technologies including wireless sensors, inertial and feature based that can be used either alone or within an integration. This chapter also discusses the brief principles of multi-sensor integration and outlines commonly used methodologies and approaches.43601

Geospatial Technologies for Urban Mobility: Introduction

This chapter gives a broad introduction to the topics in the first part of this book. This first part considers a range of geospatial technologies for smart cities and urban mobility, and demonstrates their potential to shape the future of urban mobility. In this way, the first part prepares the ground, or the framework, for the second part that focuses on technologies for smart parking and specifically on smart parking challenges in the context of cities where private motorization is not yet saturated. Readers who are already familiar with geospatial technologies and are only interested in the parking challenges, can jump ahead to the second part after this introduction.26

Parking as a Challenge for Urban Mobility: Introduction

This part of the book collects smart city approaches to support parking, mostly focusing on the parking pressure in inner-urban areas. The presented recipes for parking information and management rely on a smart – sensor-infused, connected, digitally enhanced – urban parking infrastructure that incorporates and utilizes the smart geospatial technologies presented in the first part. It also complements the approaches presented at the end of the first part, which focused on avoiding and shifting private motorized trips in cities, and thus alleviated parking pressure. The approaches presented also weigh also their options when confronted with traffic on roads with less infrastructure and less discipline.101103

Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization

Geometric and radiometric constraints-based extraction of urban road manhole covers and their maintenance-related information using mobile laser scanning data

In recent years, mobile laser scanning (MLS) has attracted increasing interest for road network three-dimensional mapping, where one of the applications of MLS data is automation in urban road manhole covers detection and their information measurement for monitoring and management of manhole covers on regular-basis. Therefore, a multi-step method is proposed based on geometric and radiometric constraints on manhole covers for their extraction and maintenance-related information measurement. The proposed method was implemented using graphics processing unit (GPU)-based parallel computing framework with improved computation time. The method performance was evaluated using MLS datasets of two complex urban roadway test sites, which included partially overlapping objects, occluded road surfaces, and intra-class variability. The manhole covers were extracted at average completeness, correctness and quality of 96.69%, 98.32%, and 95.12%, respectively. The proposed method performed satisfactorily in the challenging cases of road manhole covers and achieves significant improvement in comparison with several state-of-the-art methods

Mapping Parking Spaces Using Crowd-Sourced Trajectories

Mapping urban parking spaces helps drivers to reduce their search and cruising for parking, thus reducing traffic, reducing emissions, and reducing total travel times. Mapped urban parking spaces can also be monitored for real-time occupancy information. But while many cities in Asia, Africa, and Latin America are experiencing a strong increase of private car use on the roads, they typically lack such reliable information regarding on-street parking spaces. Hence, in this chapter we explore globally applicable mapping methods for on-street parking locations, as a first step towards smart parking (for an alternative approach see Chapter 11).1841981