  In this paper, a complete framework for Autonomous Self Driving is
implemented. LIDAR, Camera and IMU sensors are used together. The entire data
communication is managed using Robot Operating System which provides a robust
platform for implementation of Robotics Projects. Jetson Nano is used to
provide powerful on-board processing capabilities. Sensor fusion is performed
on the data received from the different sensors to improve the accuracy of the
decision making and inferences that we derive from the data. This data is then
used to create a localized map of the environment. In this step, the position
of the vehicle is obtained with respect to the Mapping done using the sensor
data.The different SLAM techniques used for this purpose are Hector Mapping and
GMapping which are widely used mapping techniques in ROS. Apart from SLAM that
primarily uses LIDAR data, Visual Odometry is implemented using a Monocular
Camera. The sensor fused data is then used by Adaptive Monte Carlo Localization
for car localization. Using the localized map developed, Path Planning
techniques like "TEB planner" and "Dynamic Window Approach" are implemented for
autonomous navigation of the vehicle. The last step in the Project is the
implantation of Control which is the final decision making block in the
pipeline that gives speed and steering data for the navigation that is
compatible with Ackermann Kinematics. The implementation of such a control
block under a ROS framework using the three sensors, viz, LIDAR, Camera and IMU
is a novel approach that is undertaken in this project.
