During navigation, a pedestrian needs to recognize a landmark at a certain decision point. If a potential landmark located at a decision point is complicated to recognize, the complexity of the decision point is significantly increased. Thus, it is important to compute routes that avoid complicated decision points (CDPs) but still achieve optimal navigation performance. In this paper, we propose an approach for computing routes that avoid CDPs while optimizing the performance of landmark-based pedestrian navigation. The approach includes (1) a model for identifying CDPs based on the structures of pedestrian networks and landmark data in real scenes, and (2) a modified genetic algorithm for computing routes that avoid the identified CDPs and find the shortest route possible. To demonstrate the advantages and effectiveness of the proposed approach, we conducted an empirical study on the pedestrian network in a real-world scenario. The experimental results show that our approach can effectively avoid CDPs while still minimizing travel distance. Furthermore, our approach can provide the routes with the shortest travel distance if the distances of the routes without CDPs exceed a certain threshold.

Humans often describe places by landmarks in natural language. However, there is no appropriate digital representation of human concepts of place – a so far elusive concept – for use in information systems. This paper proposes and tests a formal model to capture the concept of place in human perception by transferring the concept of fingerprints from robotics: a place being characterized by the landmarks around it. For the purpose of realistically testing whether places can be described by fingerprints, an algorithm for computing places from fingerprints and, independently, an in situ survey to capture these places and their fingerprints were designed. The results indicate that the union of all locations with the same fingerprint characterizes a ‘place’ in human cognition.