Landmark-based navigation systems have proven benefits relative to traditional turn-by-turn systems that use street names and distances. However, one obstacle to the implementation of landmark-based navigation systems is the complex challenge of selecting salient local landmarks at each decision point for each user. In this paper, we present Pharos, a novel system that extends turn-by-turn navigation instructions using a single global landmark (e.g. the Eiffel Tower, the Burj Khalifa, municipal TV towers) rather than multiple, hard-to-select local landmarks. We first show that our approach is feasible in a large number of cities around the world through the use of computer vision to select global landmarks. We then present the results of a study demonstrating that by including global landmarks in navigation instructions, users navigate more confidently and build a more accurate mental map of the navigated area than using turn-by-turn instructions.
While one of the earliest mobile guides, the DeepMap system @cite_18 , used a wrist-mounted display, wearable devices have become more relevant in research in the last five years. Indeed, in the context of pedestrian navigation, wearable devices do have an important benefit over their mobile counterparts as the user's hands can remain mostly free. @cite_43 introduced StripeMaps, a cartographic approach for indoor navigation with smartwatches, which transforms 2D maps with route information into a 1D stripe. McGookin and Brewster @cite_30 investigated unidirectional navigation for runners by designing a navigation system called RunNav, which could also be used on a smartwatch. RunNav does not provide explicit routes, but rather a high-level overview to inform runners of areas that are good and bad places to run.
@cite_30 @cite_43 @cite_18
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Map applications for smartwatches present new challenges in cartography, a domain in which large display sizes have significant advantages. In this paper, we introduce StripeMaps, a system that adapts the mobile web design technique of linearization for displaying maps on the small screens of smartwatches. Just as web designers simplify multiple column desktop websites into a single column for easier navigation on mobile devices, StripeMaps transforms any two-dimensional route map into a one-dimensional "stripe". Through a user study, we show that this simplification allows StripeMaps to outperform both traditional mobile map interfaces and turn-by-turn directions for pedestrian navigation using smartwatches. In addition to introducing StripeMaps, this paper also has a secondary contribution. It contains the first empirical comparison of different approaches for pedestrian smartwatch navigation and illuminates their pros and cons. We present an analysis of how runners navigate the environment when running. Results from 153 questionnaire, and 8 interview participants found navigation was often not pre-planned, had no clear route and changed organically as running was undertaken. From this we present the design of RunNav, a novel navigation system that uses Foursquare to provide support for the navigation practices runners employ. We illustrate its basic design, and plans for its future development and evaluation. In this paper, we discuss application possibilities of augmented reality technologies in the field of mobility support for the deaf blind. We propose the navigation system called virtual leading blocks for the deaf-blind, which consists of a wearable interface for Finger-Braille, one of the commonly used communication methods among deaf-blind people in Japan, and a ubiquitous environment for barrier-free application, which consists of floor-embedded active radio-frequency identification (RFID) tags. The wearable Finger-Braille interface using two Linux-based wristwatch computers has been developed as a hybrid interface of verbal and nonverbal communication in order to inform users of their direction and position through the tactile sensation. We propose the metaphor of "watermelon splitting" for navigation by this system and verify the feasibility of the proposed system through experiments. This paper presents a new real-time hierarchical (topological metric) simultaneous localization and mapping (SLAM) system. It can be applied to the robust localization of a vehicle in large-scale outdoor urban environments, improving the current vehicle navigation systems, most of which are only based on Global Positioning System (GPS). Then, it can be used on autonomous vehicle guidance with recurrent trajectories (bus journeys, theme park internal journeys, etc.). It is exclusively based on the information provided by both a low-cost, wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local submaps identified by the so-called fingerprints (vehicle poses). In this submap level (low-level SLAM), a metric approach is carried out. There, a 3-D sequential mapping of visual natural landmarks and the vehicle location orientation are obtained using a top-down Bayesian method to model the dynamic behavior. GPS measurements are integrated within this low-level improving vehicle positioning. A higher topological level (high-level SLAM) based on fingerprints and the multilevel relaxation (MLR) algorithm has been added to reduce the global error within the map, keeping real-time constraints. This level provides nearly consistent estimation, keeping a small degradation with GPS unavailability. Some experimental results for large-scale outdoor urban environments are presented, showing an almost constant processing time.