2-D UAV Navigation Solution with LIDAR Sensor under GPS-denied Environment
This project will be a simulation-based project where it will be focussing on developing 2-D LIDAR SLAM code to help Unmanned Aerial Vehicle (UAV) to navigate through a GPS-denied environment and to improve the existing SLAM algorithm by increasing the data processing speed for real time application.
The main purpose of this research is to find a solution for UAV to be able to navigate in a GPS denied surrounding without affecting flight performance. There are two ways to overcome these challenges such as using visual odometry (VO) or by using simultaneous localisation and mapping (SLAM). VO is well known to examine the motion that is created based on the sequence of images that was captured by the camera sensor to identify the position and orientation of the vehicle. On the other hand, SLAM can localise itself and map the surrounding of the environment simultaneously by using a light detection and ranging (LIDAR) sensor. However, VO has a drawback because camera sensors requires good lighting which will affect the performance of the UAV when it is navigating through a low light intensity environment. Hence LIDAR SLAM will be used as a solution to help UAV to navigate through a GPS-denied environment. In this project the main objective is to develop a 2-D SLAM code and to improve the existing 2-D SLAM algorithm by increasing the data processing speed for real time application. The expected outcome of this research is to generate LIDAR data to carry out 2-D LIDAR SLAM to navigate the drone in a 2-D plane map by using the navigation simulator that was developed in MATLAB and to increase the processing speed of the 2-D LIDAR SLAM by removing the loop closure from the 2-D SLAM algorithm. There are two methods to analyse the results. The first method is by comparing the 2-D SLAM mapping with ground truth and the second method is by comparing the UAV trajectory of the 2-D SLAM with the UAV trajectory of ground truth.