NaviGuide

Inspiration

We were inspired by the 61.7% of disabled people who avoid going into random buildings. They endup requesting for help which they shouldn't if they could navigate indoor spaces starting with ALU

What it does

It is an indoor navigation application that gets the user's location using a WIFI fingerprinting strategy, as these are already installed in each ALU class. We want to scan the nearby WiFis, and compare it to a WiFi fingerprint database we took to deduce the user's location.

In the future, we plan to add BLE (Bluetooth Low Energy) beacons nearby, which your phone can search for and hence help deduce your location with an accuracy close to meters.

The application will calculate an optimal navigation path using the Dijkstra's and/or A* algorithms while taking into account the preferences of the user,such as avoiding stairs or routing away from out-of-service operators.

The application with show the navigation path to the user on a map and in an AR view, showing step by step navigation paths, in an immersive way.

The application also allows a student to link their Calendar, which will show all their upcoming classes and office hours, allowing them to navigate to their classes.

The application will also feature a service directory, showing all campus services such as offices for various campus staff such as The Registrar, Centre for Entrepreneurship, Security Office and more, allowing students to quickly navigate to them.

We also have a chatbot that prompts the user where they want to go, and will deduce their destination.

How we built it

We created a React Native application, that is compatible with both iOS and Android, allowing students to access all this vital information in a streamlined way.

Challenges we ran into

The libraries we used for precise geolocation and WiFi fingerprinting (OpenHPS) was very badly documented.

We also couldn't compile the necessary libraries for implementing the AR navigation because of underpowered machines.

Accomplishments that we're proud of

We were able to develop an application that integrates the student's calendar, a machine learning model that processes the user's requests and direct them to their classes or rooms.