IntelliMap: AI-Driven Terrain Exploration and Mapping

This study aims to explain the rationale and necessity of modelling 3D images of different landscapes and terrains in order to analyse their features and gain an overview of them using real-time techniques. As soldiers and intelligence agencies are known to constantly spy on one another, being able to see the topography and geography of their enemy's region in three dimensions would be extremely advantageous to them. In essence, these are all raised maps that are frequently utilised for mobile robot navigation in unfamiliar and unstructured settings. The navigator's primary responsibility is to update elevation maps dynamically in real time. The real-time photos we will be feeding our model are all the input data we receive from the security cameras mounted on it and the drone. Real-time mapping technology for digital elevation maps (DEMs) allows to comprehend the deformation of the continental crust at length scales of few metres. The combination of interactive rendering and interactive mapping directly onto the 3D surface, with the flexibility to travel the landscape and change viewpoints at will during mapping, is the primary strength of a model that transforms or provides a picture with 3D raised models. Consequently, several perspectives of the same landscape region or terrain can be viewed. The automatic extraction of different types of information from images is the focus of computer vision. Allowing machines to see and interact with the world as people do is the primary goal of machine vision. However, the military and intelligence services can spy in enemy territory thanks to the advantages of computer vision. An army needs precise and accurate information about every move it will make as well as the surrounding conditions, as the latter are crucial to the operation or mission. For their operation to be successful, therefore, these real-time 3-D models that they have access to are crucial. They can interact with these three-dimensional models and extract any feature they wish to use. Extraction the features of images and subsequently to analyse is an important activity where CNN is used. Additionally, we train the dataset using triangulation to improve the accuracy of the model and use it for feature matching descriptors to obtain higher accuracy. The efficiency and accuracy of all 3-D models can be increased by using numerous cameras or images to collect different perspectives of an object, followed by the application of CNN and matching descriptors. In that scenario, each camera requires calibration as well as extrinsic data.