Way-finding could be a very sticky problem when people arrive in a completely new environment. We worked with CIMS to acquire the IBM model for precision engineering of buildings from Carleton University, and based on this we imported it into the game engine to develop a solution for indoor navigation.
Xinpeng Liu: Project Manager & Engineer
Yuchen Hu: Engineer
Xueqiao Liu: UI&UX designer
Krystal Shao: Designer & Accessiblity Manager
September 2022 - March 2023
CUFind is an initiative aimed at improving navigation and accessibility within the university's buildings. With a growing campus and increasing foot traffic, finding your way around can be a challenge for both students and visitors. This project aims to provide a comprehensive and user-friendly solution for indoor navigation, utilizing modern technology and design to create a seamless experience. Whether you're looking for a specific classroom, office, or facility, the Carleton University Indoor Wayfinding system will make getting around a breeze.
Take a look at our promotional video. Please write to [email protected] to get the demo application.
The motivation behind the development of the CU Find application stems from the growing need for effective indoor wayfinding solutions in large, complex buildings. Navigating these spaces can be challenging and time-consuming for users, especially for those who are unfamiliar with the layout or have cognitive or mobility impairments. Traditional means of indoor navigation, such as static signage and maps, often prove inadequate in addressing the unique challenges of indoor environments, leading to frustration and disorientation among users.
The CU Find project was efficiently transformed using the IFC files provided by CIMS through the Datasmith plug-in. Rather than splitting the original scene components (solid body based) into separate FBX chunks (meshes based), Datasmith devised a novel import solution that re-utilizes assets and textures to minimize workload. Although this approach sacrifices some of the original accuracies, the primary objective of CU Find is to establish a cost-effective workflow. By pre-processing the layering in Revit, we can achieve a layered view during the import process into the game engine instead of treating the whole building as a heavy entity.
The implementation of an effective wayfinding algorithm is crucial for indoor navigation systems. One widely-used approach is the A* (A-star) algorithm, which is a pathfinding and graph traversal algorithm that relies on nodes to determine the shortest path between two points. The A* algorithm is a popular choice for wayfinding applications due to its efficiency and accuracy in determining the shortest path in various environments. It is an extension of Dijkstra’s algorithm and uses a heuristic approach to estimate the cost of reaching the destination from a given node. By combining the actual cost of reaching a node from the starting point with the heuristic estimate of the cost from that node to the destination, the A* algorithm effectively balances exploration and exploitation during pathfinding.
