A proposal and preference evaluation of route line design for Augmented Reality (AR)

• Published in: Boletim de Ciências Geodésicas Vol. 30; pp. 1 - 15
• Main Authors: Amorim, Fabricio Rosa, Schmidt, Marcio Augusto Reolon
• Format: Journal Article
• Language: English
• Published: Curitiba Universidade Federal do Paraná, Centro Politécnico 01.01.2024
• Subjects: Animation; Augmented reality; Cartography; Cognitive load; Color; Design; Knowledge representation; Localization; Navigation systems; Perception; Symbols; Virtual reality; Visual tasks; Visualization
• ISSN: 1413-4853; 1982-2170
• DOI: 10.1590/sl982-21702024000100014

Augmented Reality (AR) seamlessly integrates the real environment with virtual objects, enriching users' information perception. The application of AR in personal and vehicular navigation tasks facilitates the evolution of navigation systems from two-dimensional (2D) to three-dimensional (3D) representations, incorporating an egocentric viewpoint. Spatial knowledge, specific navigation objectives, navigational skills, and the navigational symbols plays pivotal roles in map utilization, encompassing self-localization, proximity, navigation, and event awareness. The representation of symbols in navigation tasks relies on static and dynamic or animated visual cartographic variables. In this context, our research focuses on evaluating symbols used for depicting routes and landmarks in AR systems during personal navigation tasks. The experiments investigated the representation of routes, exploring variations in route line thickness, the presence or absence of border-color, fill color, and the speed of arrows moving along the route line. The findings of the study reveal that thinner route lines significantly contribute to enhance perception of surroundings, blue fill color exhibit superior performance compared to traditional navigation system colors, and volunteers noted that the arrow animation speed noticeably affected their perceptions of elements within the scenario. These insights contribute to understanding of how visual variables influence user preferences and experiences in AR-based navigation tasks.