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.
In general, landmarks have to be chosen carefully as their selection is highly important for the resulting quality of navigation instructions @cite_23 (e.g. the personal salience of a landmark could be effected by language or gender @cite_19 ). More specifically, Sorrows and Hirtle @cite_54 examined landmarks in real and electronic spaces and classified landmarks in terms of visual, cognitive and structural dimensions. Global landmarks have to be outstanding in all dimensions. Steck and Mallot @cite_27 investigated the role of global and local landmarks in virtual environment navigation. They found out that in virtual environments both local and global landmarks are used for wayfinding, but different participants rely on different strategies; some of the participants used only local landmarks while others only used global ones. This results in a key question for our work, as with Pharos we aim at improving pedestrian navigation instructions with only a single global landmark rather than multiple local landmarks.
@cite_19 @cite_54 @cite_27 @cite_23
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In visual navigation, landmarks can be used in a number of different ways. In this paper, we investigate the role of global and local landmarks in virtual environment navigation. We performed an experiment in a virtual environment called “Hexatown”, consisting of a regular hexagonal grid of streets and junctions. Each junction was identified by the presence of distinct local landmarks (buildings, phone box, and so on). Additionally, compass information or a global frame of reference was provided by global landmarks (hilltop, television tower, and city skyline). According to participants' movement decisions, egomotion was simulated, and displayed on a 180 deg. projection screen. Participants learned the route back and forth between two local landmarks. In the test phase, individual junctions were approached and the participant's movement decision was recorded. We performed two experiments involving landmark changes after learning. In the first, we used conflicting cues by transposing landmarks. In the second experiment, we reduced either local or global landmark information. Results show that both local and global landmarks are used in wayfinding decisions. However, different participants rely on different strategies. In the first experiment (cue conflict) for example, some of the participants used only local landmarks while others relied exclusively on global landmarks. Other participants used local landmarks at one location and global landmarks at the other. When removing one landmark type in the second experiment, the other type could be used by almost all participants, indicating that information about the neglected landmark type was present in memory. Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method. Landmarks are a key element in navigation and have been used extensively to provide navigation support to pedestrians through mobile devices in urban areas. Natural environments differ significantly from built environments in a number of ways including, for example, the degree of structure and regularity, the types and density of landmarks, and the way in which people navigate in those environments. In this paper, we investigate how people currently navigate 'in the wild', and whether landmark-based navigation support through mobile devices is a feasible option in such settings. We present results from two studies: a questionnaire-based study focussing on current practice and the use of landmarks, and a qualitative lab-based study using immersive panoramic photographs and photographs of (natural) landmarks investigating the feasibility of landmark-based navigation support in natural environments. The results indicate that a small number of means are currently used for navigation in the wild, and that certain types of landmarks might be feasible for navigation support on mobile devices. We also found initial evidence that immersive panoramic photographs may constitute a promising way to evaluate mobile applications. Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address these issues. First, a comprehensive list of landmarks is mined from two sources: (1) 20 million GPS-tagged photos and (2) online tour guide Web pages. Candidate images for each landmark are then obtained from photo sharing Websites or by querying an image search engine. Second, landmark visual models are built by pruning candidate images using efficient image matching and unsupervised clustering techniques. Finally, the landmarks and their visual models are validated by checking authorship of their member images. The resulting landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. The experiments demonstrate that the engine can deliver satisfactory recognition performance with high efficiency.