Urban Positioning on a Smartphone: Real-time Shadow Matching Using GNSS and 3D City Models

The performance of global navigation satellite system (GNSS) user equipment in urban canyons is particularly poor in the cross-street direction. This is because more signals are blocked by buildings in the cross-street direction than along the street [1]. To address this problem, shadow matching has been proposed to improve cross-street positioning from street-level to lane-level (meters-level) accuracy using 3D city models. This is a new positioning method that uses the city model to predict which satellites are visible from different locations and then compares this with the measured satellite visibility to determine position [2]. In previous work, we have demonstrated shadow matching using GPS and GLONASS data recorded using a geodetic GNSS receiver in Central London, achieving a cross-street position accuracy within 5m 89% of the time [3]. This paper describes the first real-time implementation of shadow matching on a smartphone capable of receiving both GPS and GLONASS. The typical processing time for the system to provide a solution was between 1 and 2 seconds. On average, the cross-street position accuracy from shadow matching was a factor of four better than the phone’s conventional GNSS position solution. A number of groups have also used 3D city models to predict and, in some cases, correct non-line-of-sight reception [4-6]. However, to our knowledge, this paper reports the first ever demonstration of any 3D-model-aided GNSS positioning technique in real time, as opposed to using recorded GNSS data. When it comes to real-time positioning on a smartphone, various obstacles exist including lower-grade GNSS receivers, limited availability of computational power, memory, and battery power. To tackle these problems, in this work, an efficient smartphone-based shadow-matching positioning system was designed. The system was then implemented in an app (i.e. application or software) on the Android operating system, the most common operating system for smartphones. The app has been developed in Java using Eclipse, a software development environment (SDE). It was built on Standard Android platform 4.0.3, using the Android Application programming interface (API) to retrieve information from the GNSS chip. The new positioning system does not require any additional hardware or real-time rendering of 3D scenes. Instead, a grid of building boundaries is computed in advance and stored within the phone. This grid could also be downloaded from the network on demand. Shadow matching is therefore both power-efficient and cost-effective. Experimental testing was performed in Central London using a Samsung Galaxy S3 smartphone. This receives both GPS and GLONASS satellites and has an assisted GNSS (AGNSS) capability. A 3D city model of the Aldgate area of central London, supplied by ZMapping Ltd, was used. Four experimental locations with different building topologies were selected on Fenchurch Street, a dense urban area. Using the Android app developed in this work, real-time shadow-matching positioning was performed over 6 minutes at each site with a new position solution computed every 5 seconds using both GPS and GLONASS observations were used for real-time positioning. The measurement data was also recorded at 1-second intervals for later analysis. Various criteria are applied to access the new system and compare it with the conventional GNSS positioning results. The experimental results show that the proposed system outperforms the conventional GNSS positioning solution, reducing the mean absolute deviation of the cross-street positioning error from 14.81 m to 3.33 m, with a 77.5 percentage reduction. The feasibility of deploying the new system on a larger scale is also discussed from three perspectives: the availability of 3D city models and satellite information, data storage and transfer requirements, and demand from applications. This meters-level across-street accuracy in urban areas benefits a variety of applications from Intelligent Transportation Systems (ITS) and land navigation systems for automated lane identification to step-by-step guidance for the visually impaired and for tourists, location-based advertisement (LBA) for targeting suitable consumers and many other location-based services (LBS). The system is also expandable to work with Galileo and Beidou (Compass) in the future, with potentially improved performance. In the future, the shadow-matching system can be implemented on a smartphone, a PND, or other consumer-grade navigation device, as part of an intelligent positioning system [7], along with height-aided conventional GNSS positioning, and potentially other technologies, such as Wi-Fi and inertial sensors to give the best overall positioning performance. / References [1] Wang, L., Groves, P. D. & Ziebart, M. Multi-constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models. Journal of Navigation, July 2012. [2] Groves, P. D. 2011. Shadow Matching: A New GNSS Positioning Technique for Urban Canyons The Journal of Navigation, 64, pp417-430. [3] Wang, L., Groves, P. D. & Ziebart, M. K. GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Prediction Scoring. ION GNSS 2012. [4] Obst, M., Bauer, S. and Wanielik, G. Urban Multipath Detection and mitigation with Dynamic 3D Maps for Reliable Land Vehicle Localization. IEEE/ION PLANS 2012. [5] Peyraud, S., Bétaille, D., Renault, S., Ortiz, M., Mougel, F., Meizel, D. and Peyret, F. (2013) About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm. Sensors, Vol. 13, 2013, 829?847. [6] Bourdeau, A., M. Sahmoudi, and J.-Y. Tourneret, “Tight Integration of GNSS and a 3D City Model for Robust Positioning in Urban Canyons,” Proc. ION GNSS 2012. [7] Groves, P. D., Jiang, Z., Wang, L. & Ziebart, M. Intelligent Urban Positioning using Multi-Constellation GNSS with 3D Mapping and NLOS Signal Detection. ION GNSS 2012

A vision transformer approach for traffic congestion prediction in urban areas

Traffic problems continue to deteriorate because of the increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNNs) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to the CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use

Analysing the effects of sensor fusion, maps and trust models on autonomous vehicle satellite navigation positioning

This thesis analyzes the effects of maps, sensor fusion and trust models on autonomous vehicle satellite positioning. The aim is to analyze the localization improvements that commonly used sensors, technologies and techniques provide to autonomous vehicle positioning. This thesis includes both survey of localization techniques used by other research and their localization accuracy results as well as experimentation where the effects of different technologies and techniques on lateral position accuracy are reviewed. The requirements for safe autonomous driving are strict and while the performance of the average global navigation satellite system (GNSS) receiver alone may not prove to be adequate enough for accurate positioning, it may still provide valuable position data to an autonomous vehicle. For the vehicle, this position data may provide valuable information about the absolute position on the globe, it may improve localization accuracy through sensor fusion and it may act as an independent data source for sensor trust evaluation. Through empirical experimentation, the effects of sensor fusion and trust functions with an inertial measurement unit (IMU) on GNSS lateral position accuracy are measured and analyzed. The experimentation includes the measurements from both consumer-grade devices mounted on a traditional automobile and high-end devices of a truck that is capable of autonomous driving in a monitored environment. The maps and LIDAR measurements used in the experiments are prone to errors and are taken into account in the analysis of the data

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Efficient Semantic Segmentation for Resource-Constrained Applications with Lightweight Neural Networks

This thesis focuses on developing lightweight semantic segmentation models tailored for resource-constrained applications, effectively balancing accuracy and computational efficiency. It introduces several novel concepts, including knowledge sharing, dense bottleneck, and feature re-usability, which enhance the feature hierarchy by capturing fine-grained details, long-range dependencies, and diverse geometrical objects within the scene. To achieve precise object localization and improved semantic representations in real-time environments, the thesis introduces multi-stage feature aggregation, feature scaling, and hybrid-path attention methods