## Inspiration

In the modern, technology-driven world, indoor navigation has remained largely unexplored and untamed. Inspired by the need to provide a hassle-free and accurate indoor navigation system, we conceptualized Innav. The idea stemmed from a vision to exploit the untapped potential of phone sensors, steering clear of traditional WPS and Bluetooth technologies, which have their own limitations and privacy concerns.

## What We Learned

Throughout the development of Innav, we delved deep into the intricacies of mobile sensor technology. We learned how to harness the power of various phone sensors to create a seamless navigation system. Furthermore, our research and development phase educated us about the complexities involved in indoor mapping and routing, pushing us to innovate and devise intelligent algorithms that would enhance user experience through precise localization.

## How We Built It

The Innav project was built through a collaborative and iterative process. We developed a sophisticated algorithm that uses your phone's sensors like the accelerometer, gyroscope, and magnetometer to pinpoint your indoor location with unprecedented accuracy. Through a robust app interface, users can easily navigate complex indoor spaces without the need for internet connectivity or additional hardware installations.

We implemented agile methodologies to facilitate a smooth development process, encouraging feedback and continual improvements. By fostering a culture of innovation and learning, our team managed to build a solution that bridges the gap in the indoor navigation market.

## Challenges We Faced

While conceptualizing and building Innav, we faced a series of challenges. The first major hurdle was ensuring the accuracy and reliability of sensor-derived data, which required extensive testing and calibration. Creating a comprehensive and detailed indoor mapping system also presented a significant challenge, requiring meticulous planning and execution.

Additionally, we grappled with the task of making the app user-friendly, ensuring that individuals of all tech-savviness levels could navigate with ease. Through perseverance and a passion for innovation, we managed to overcome these challenges, paving the way for a revolutionary product that promises to redefine indoor navigation.

## Accomplishments We're Proud Of

Throughout our journey, we achieved several milestones that fuel our pride. Firstly, we successfully revolutionized the concept of indoor navigation by developing a system that operates without WPS or Bluetooth, making it a pioneer in the sector. Our algorithm has proven to be highly accurate, earning accolades and recognition from industry experts. Additionally, the user-centric design of our app has garnered positive feedback for its ease of use and intuitive interface.

We're also immensely proud of our team's synergistic efforts, which combined a range of skills and expertise to develop a groundbreaking product. The resilience and innovation displayed by our team have not only propelled Innav to new heights but also contributed to shaping a new era in indoor navigation solutions.

## What's Next for Innav

Looking ahead, we envision Innav evolving into a comprehensive platform that integrates with various services, enhancing the user's indoor experience further. We plan to collaborate with business complexes, shopping malls, and educational institutions to incorporate Innav into their infrastructure, offering visitors an effortless navigation experience.

Furthermore, we aim to expand our mapping database, adding detailed layouts of numerous indoor spaces worldwide. We are also looking into potential partnerships and integrations with augmented reality technologies to provide users with a more immersive and interactive navigation experience.

We are committed to continuous innovation, and with upcoming updates, users can look forward to an even more enriched, feature-packed, and intuitive Innav experience, setting new benchmarks in the indoor navigation sector.
## Inspiration💡

Tired of mall mazes and endless searches for the elusive restroom? Beyond Navigation is here to save the day! Inspired by the comical struggles of our team member in a race against time with nature, we've crafted a out-of-the-box solution for internal navigation, no GPS required. Say goodbye to wandering and hello to efficiency! #HackTheMaze #BeyondNavigation

## What it does 🔎

Inspired by the convenience of navigation kiosks, our solution tries to bring a similar kind of functionality without the need for GPS, dedicated kiosks or expensive internal map technology. Here are the key points on how our whacky solution tackles the maze problem:

* Leverages unnoticed evacuation / floor plans mandated in most buildings to generate a map outline of the entire floor
* Users can simply type in their desired destination and the chatbot generates the best path to reach the location
* Unlike traditional apps cluttering your phone, our solution is a WhatsApp chatbot, making is cross-platform on both Android and iOS devices and ensures instant, hassle-free access to internal navigation, right within your familiar WhatsApp interface

## How we built it 🔨

* **WhatsApp + Infobip API** - We adopted to go for a conversational UI via Whatsapp, improving reach across mobile platforms and eliminating the need for additional app downloads.
* **OpenCV + pathfinding algorithms**- Determine general layout of the floor from the image and to calculate the shortest path.
* **Google Cloud Storage** - Secure storage of user images as well as computed data.
* **Google Cloud Vision AI API** - Used to identify rooms in the floor plan and determine their positions accurately. This integration allows the user to dynamically query the map
* **Google Cloud Run Platform** - A low-cost serverless computing environment where we run our backend

## Challenges we ran into⚠️

* **OpenCV itself**: Processing images was quite a challenge, especially when we used live images from WhatsApp camera. The quality of images varied, making the processing part pretty tricky.
* **New technologies**: We want to challenge ourselves with choosing technologies out of our comfort zone. Most of the technologies that we have used in this project were something new to us
* **Comprehensive Google Cloud Usage**: Hosting the whole solution on Google Cloud was a first for us. Learning to navigate through the quirks of Google Cloud became a crucial part of our hackathon journey

## Accomplishments that we're proud of🥇

* **Complete prototype solution**: We built a prototype that actually works! It's not just an idea; it's somthing ready to help you find your way around
* **Smart Tech Choices, No Big Costs**: LLM models is a versatile tool, capable of even ingesting however, it is extremely expensive to run, especially at scale. We didn't go for fancy and expensive tech that might sound cool. Instead, we chose practical and budget-friendly technologies.
* **Optimizations**: For real-time messaging, we needed really quick response times. Initially, our application took 45 seconds to process the pathfinding algorithm which was unacceptable. We dug deep and found a significantly more efficient albeit slightly less accurate Python library which did the same in 2-3 seconds bringing our overall processing time to ~7 seconds.

## What we learned 🧠

* **Dealing with user input**: We learned a lot about how unpredictable user input as well as intermediate systems can be (WhatsApp image compression looking at you 👀)
* **Image Manipulation**: We learnt new ways to interact with images, the pipeline used to extract useful information from them as wells as the nuances on working with them

## What's next for Beyond Navigation 💭

Although the idea originated from the nuisance of new surroundings, we discovered that this idea can greatly improve universal accessibility by including features that can create a personalized path to accommodate disability constraints to optimize route planning. To improve the accuracy of our pathways, we plan on implementing Bluetooth and Wifi. Additionally, we wanted to use the phone’s magnetometer and pedometer to enhance user guidance precision.
# cheqout

A project for QHacks 2018 by [Angelo Lu](https://github.com/angelolu/), [Zhijian Wang](https://github.com/EvW1998/), [Hayden Pfeiffer](https://github.com/PfeifferH/) and [Ian Wang](https://github.com/ianw3214/). Speeding up checkouts at supermarkets by fitting existing carts with tech enabling payments.

### Targeted Clients: Established supermarket chains

Big box supermarkets with established physical locations, large inventories and high traffic, such as Walmart and Loblaw-branded stores.

### Problems Identified: Slow checkouts

Checkout times can be slow because they are dependent on available checkout lanes and influenced by factors such as the number of items of the people in front of you and speed of the cashier and the person in scanning and paying. Stores with consistently slow checkouts are at risk of losing customers as people look for faster options such as other nearby stores or online shopping.

### Possible Solutions

1. **Additional checkout lanes**
This solution is limited by the physical layout of the store. Additional checkout lanes require additional staff.
2. **Self-checkout kiosks**
This solution is also limited by the physical layout. Lines can still develop as people must wait for others to scan and pay, which may take a long time.
3. **Handheld self-scanners**
In supermarkets, products such as produce, and bulk foods are weighed, which cannot be processed easily with this solution.
4. **Sensor-fusion based solution replacing checkouts (Ex. Amazon Go)**
In large chain supermarkets with many established locations, implementation of all the cameras, scales and custom shelving for the system requires massive renovations and is assumed to be impractical, especially considering the number of products and stores.

### Proposed Solution: “cheqout”, Smart Shopping Carts

The solution, titled “cheqout” involves fitting existing shopping carts with a tray, covering the main basket of the cart, and a touchscreen and camera, on the cart handle.
A customer would take out a cart upon entry and scan each items’ barcode before placing it in the cart. The touchscreen would display the current total of the cart in real time. Once the customer is ready to pay, they can simply tap “Check Out” on the touchscreen and scan a loyalty card (virtual or physical) with an associated payment method or tap their credit or debit card directly. Alternatively, the customer can proceed to a payment kiosk or traditional checkout if they do not have an account, are paying in cash or want a physical receipt, without having to wait for each item to be scanned again.
On the way out, there would be an employee with a handheld reader displaying what has been paid in the cart to do a quick visual inspection.
This solution trusts that most users will not steal products, however, a scale integrated in the tray will continuously monitor the weight of products in the cart and the changes in weight associated with an item’s addition/removal and prompt accordingly. The scale will also be used to weigh produce.
This solution allows for the sale of weighed and barcoded items while still decreasing checkout line congestion. It is scalable to meet the requirements of each individual store and does not require the hiring of many additional personnel.

### Challenges

* Time: This project is being built for QHacks, a 36-hour hackathon
* Technical: The scale and touch screen remain uncooperative, prompting temporary fixes for the purpose of demonstration

### Possible Future Features/Value-Added Features

* Information about traffic within the store
By implementing indoor location tracking, analysts can visualize where customers frequent and tailor/adjust product placement accordingly
* Information about product selection
The system can record a customer’s decision making process and uncertainties based on how long they spend in one spot and if they add, remove or swap items from their cart