# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 4 Smart Mobility Services and Applications
### Outdoor Navigation Technologies
Dead reckoning is also being used in terms of navigation and vehicle location [9], where it is possible to install odometer and optical sensors for wheel direction detection and achieve a low-cost solution, however, keeping the problem of error accumulation. Visual positioning is a technique that aims to overcome the imprecision of outdoor positioning obtained with previous mentioned technologies using visual elements such as markers, landmarks combined with camera images captured by a smartphone. Google is currently developing its Visual Positioning System (VPS) which is integrated with Google Maps so that, based on augmented reality, navigation takes on another dimension while using the camera to inspect the surroundings, infer position and adjust positioning and navigation recommendations [112]. This is achieved through considerable computational power combined with a large set of data on the backend that allows the recognition of the position based on the image captured by the camera of the mobile phone, mostly through buildings and tourist spots. This approach is focused on the use of known landmarks (momuments, etc.) that can therefore quickly and unambiguously, or at least with great precision, identify the user's current position. Some of the advantages of this approach include [112; 113]: higher accuracy than GPS, and therefore an alternative for locations with tall buildings, added value for blind people, whether on the streets, at a school or in an establishment, useful for situations where a person is lost without knowing their location, added value for companies, who can use this system to provide navigation information to their customers to travel to their establishment, either on foot or using public transport, the availability of application in indoor and outdoor environments, high scalability and low cost. Like all technologies, this one too has its limitations and challenges, which include difficulty, in some locations, to have landmarks that can be used to accurately identify a particular location, difficulty in mapping the interior of all buildings, vulnerability to luminosity, causing shadows with impact on the identification of places, and the possibility of people or objects being able to block the captured image and thus prevent the recognition of the location. Passive markers are being used to provide an optimized route that goes through several points of interest. The current location can be obtained through passive markers that are strategically placed near points of interests which have been previously mapped to a Spatial Map Graph, using machine learning [114]. Passive markers, in an outdoor environment, might be represented through QR codes that are placed in places such as lamp posts or trees. An example of visual landmark-based localization is reported in [115], where the authors present a study on location based on geo-referenced landmarks, whose images are collected by cameras in order to serve as an input to the location functionality that allows the attainment of the precise location of micro aerial vehicles in an urban environment. The use of vision/landmark-based positioning is also reported in [116],where the authors describe two different ways of acting: on the one hand, what they call \"single snapshot-based positioning\", which consists of obtaining an image and comparing it with a set of images previously taken and georeferenced through an image matching algorithm. On the other hand, they present a scenario based on \"continuous localization\", which is combined with a dead reckoning compass allowing an accuracy of 10 to 15 m.