An approach for computing routes without complicated decision points in landmark-based pedestrian navigation

ABSTRACT

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.

KEYWORDS:

• Landmark
• pedestrian navigation
• decision point
• route planning
• genetic algorithm

Acknowledgments

The authors are grateful to Professor Bo Huang and the anonymous reviewers for providing their valuable comments on this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. https://map.baidu.com.

2. https://www.google.com/maps/streetview.

3. https://www.microsoft.com/maps/streetside.aspx.

Additional information

Funding

The work described in this paper is supported by the National Natural Science Foundation of China [Grant No. 41371422, 41701395, 41701449].

Notes on contributors

Sha Zhou

Sha Zhou is a lecturer at the Xinyang Normal University. His research interests include place descriptions, human wayfinding and navigation services. Email: [email protected].

Run Wang

Run Wang is a lecturer at the China University of Geosciences. Her research interests cover spatial analysis and urban remote sensing. Email: [email protected].

Junhua Ding

Junhua Ding is a professor at the University of North Texas. His research interests include data science, machine learning, software engineering, and geospatial information science. Email: [email protected].

Xiaofang Pan

Xiaofang Pan is a lecturer at the Xinyang Normal University. Her research interests include Geographic Information Systems for Transportation and spatial data mining. Email: [email protected].

Shunping Zhou

Shunping Zhou is a professor at the China University of Geosciences. His research interests cover pedestrian navigation and geographic information system. Email: [email protected].

Fang Fang

Fang Fang is an associate professor of China University of Geosciences. Her research interests cover spatial data mining, image scene classification and GIS application. Email: [email protected].

Wenjie Zhen

Wenjie Zhen is a PhD candidate at the China University of Geosciences. His research interests include indoor pedestrian navigation and road networks matching. Email: [email protected].