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.
Research on pedestrian navigation dates back almost two decades. From the beginning of this work, landmarks have played a role in guiding the user. For example, the GUIDE project @cite_21 aimed at integrating landmarks in textual navigation instructions. Malaka and Zipf @cite_18 used a 3D city model to create navigation instructions not only incorporating the visibility but also the look of landmarks (e.g. turn right after the red building'') for pre-selected situations. Similarly, the LOL@ tourist guide for mobile devices @cite_58 enhanced routing information with references to landmarks. The PhotoMap @cite_3 uses images of taken with a GPS-enhanced mobile phone as background maps for on-the-fly navigation tasks.
@cite_18 @cite_21 @cite_58 @cite_3
1480883159 2572408462 1973322906 645436802
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. Research examining pedestrian navigation systems that use landmarks to explain routes became popular in the past years. Nevertheless, it is still an open question how many landmarks should be depicted at once. In this paper a user study is presented that evaluates two different indoor navigation system designs that depict either one (N = 63) or four (N = 60) landmarks to guide the user. The time it took the participants to recognize where to go was captured as a dependent variable. Results show that the interface only depicting one landmark leads to faster self-localization. Therefore, it is argued that a pedestrian navigation system should mainly depict one highly salient landmark in a navigation instruction in order to keep navigation efficiency high. In the past people have used very different forms of directions depending on how those directions were acquired. If a person is giving another person directions in a familiar area, he will frequently use landmarks to describe the route [10]. If the person gets the route from a personal navigation system though, it will be displayed on a map and make use of street names for the directions. In this paper we present a system to automatically give landmark based navigation to pedestrians by using panoramic imagery to both find salient landmarks along a route automatically, and to present those landmarks to a pedestrian navigator in an immersive and intuitive manner. Our system primarily uses automatically detected business signs as landmarks, and currently works in a half dozen cities around the world. We have also evaluated our system and found that people can effectively navigate solely using landmark enhanced panoramas of decision points along the route. We present a heterogeneous landmark-based visual navigation approach for a monocular mobile robot. We utilize heterogeneous visual features, such as points, line segments, lines, planes, and vanishing points, and their inner geometric constraints managed by a novel multilayer feature graph (MFG). Our method extends the local bundle adjustment-based visual simultaneous localization and mapping (SLAM) framework by explicitly exploiting the heterogeneous features and their inner geometric relationships in an unsupervised manner. As the result, our heterogeneous landmark-based visual navigation algorithm takes a video stream as input, initializes and iteratively updates MFG based on extracted key frames, and refines robot localization and MFG landmarks through the process. We present pseudocode for the algorithm and analyze its complexity. We have evaluated our method and compared it with state-of-the-art point landmark-based visual SLAM methods using multiple indoor and outdoor datasets. In particular, on the KITTI dataset, our method reduces the translational error by 52.5 under urban sequences where rectilinear structures dominate the scene.