Implementation of Slam in Quadcopter

Abstract

The project is about implementing SLAM (Simultaneous Localization and Mapping) technology in quadcopters. The algorithm to be implemented is VINS-mono. This project can be divided into four main stages. 1st stage consists of understanding the drone components, dynamics, and control and making it functional for RC control. 2nd stage, the drone's autopilot (flight controller) is to be implemented. For this, real-time data from the Inertial Measurement Unit (IMU) and some peripheral sensors are combined to calculate the drone's relative orientation, velocity, and position. The data is first filtered, biased, and calibrated to get these properties. Now using these attributes, a control system will be implemented for self-stabilization and drone navigation. 3rd stage revolves around getting data, extracting features, and making a visual 3D map of the environment using the camera. 4th stage is the simultaneous integration of the second and third stages of in-flight computers. This part includes the state estimation, localization, and path planning using SLAM's algorithm, i.e., VINS-mono. Finally, the drone is made autonomous to move independently from one point to the other on the map. The drone will also map the environment around it and continuously update it using real-time data from sensors.