WAL-LEE

Problem

After building contstruction, ambient conditions tend to become immutable. These conditions can create inhospitable combinations of humidity, lighting, and temperature.

Abstract:

We propose an indoor ambient environment tracker that utilizes autonomous navigation and LiDAR mapping to enhance the comfort of individuals inside the building. The system is designed to continuously monitor and track indoor environmental factors, such as temperature, humidity, and air quality, and create a detailed 3D map of the indoor environment. This information is then used to provide real-time feedback to users, enabling them to make informed decisions to improve their comfort. The proposed system has the potential to improve the indoor environment's overall comfort and well-being, enhancing productivity, and reducing energy consumption.

Introduction:

Indoor environments significantly impact individuals' comfort, productivity, and overall well-being. The indoor environmental factors that impact these aspects include temperature, humidity, and air quality. Traditional methods of monitoring and controlling these factors are time-consuming, labor-intensive, and may not provide real-time data. Therefore, this dissertation proposes an indoor ambient environment tracker that utilizes autonomous navigation and LiDAR mapping to continuously monitor and track these factors and provide real-time feedback to enhance individuals' comfort inside the building.

Background:

LiDAR mapping and autonomous navigation are technologies that have been widely used in various industries, including robotics, surveying, and agriculture. LiDAR is a remote sensing technology that uses laser light to create a detailed 3D map of an environment. Autonomous navigation is the ability of a robot to move and navigate through an environment without human intervention.

Proposed System:

The proposed indoor ambient environment tracker consists of a mobile robot equipped with sensors and LiDAR. The robot autonomously navigates through indoor spaces, collecting data about the indoor environment in real-time. The data collection module consists of sensors that collect data about the indoor environment, including temperature, humidity, and air quality. The data is continuously collected and transmitted to the mapping module, which uses the collected data to create a 3D map of the indoor environment.

The 3D map is then used to provide real-time feedback to users about the indoor environment, including temperature, humidity, and air quality. The feedback enables users to make informed decisions, such as adjusting the temperature, opening or closing windows, or turning on air filtration systems, to enhance their comfort inside the building.

Evaluation:

To evaluate the proposed indoor ambient environment tracker, experiments were conducted in an indoor environment. The robot was programmed to navigate through the indoor space autonomously, collecting data about the indoor environment in real-time. The data collected by the robot was compared to data collected using traditional sensors, and the results showed that the proposed system was accurate and reliable.

Conclusion:

In conclusion, this dissertation proposes an indoor ambient environment tracker that utilizes autonomous navigation and LiDAR mapping to continuously monitor and track indoor environmental factors and provide real-time feedback to enhance individuals' comfort inside the building. The proposed system has the potential to improve the indoor environment's overall comfort and well-being, enhancing productivity, and reducing energy consumption. Further research could be done to optimize the system's performance and evaluate its effectiveness in different indoor environments. The proposed system has promising applications in various fields, including healthcare, commercial buildings, and residential homes.

Ubiquity Robot OS Documentation

This follows a very similar configuration to our system specification. - > https://learn.ubiquityrobotics.com/noetic_overview_need_to_know

WorkStation Setup

https://learn.ubiquityrobotics.com/noetic_quick_start_workstation

WHAT IS SLAM?!

SLAM (simultaneous localization and mapping) is a method used for autonomous vehicles that lets you build a map and localize your vehicle in that map at the same time. SLAM algorithms allow the vehicle to map out unknown environments.