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SLAM (Simultaneous Localization and Mapping) Simulation and Visualization Using ROS & RP LIDAR with RASPBERRY PI and MATLAB

The initial motivation of this project is to prototype an autonomous driving model with basic functionalities. It also known as self-driving cars, are one of the dramatic innovations in auto industry. Prototyping can help to evaluate and test the designs, including hardware and software. As a student of Electronic and Control systems, we want to apply what I have learned on such an important process. After doing some literature research on it, we found the key point of Autonomous vehicles is to let the vehicles be aware of the surrounding area, and it is realized by two technology: computer vision and sensor Technology fusion. Among the sensors used by the vehicles, lidar is used most. Therefore, my second motivation is to apply low cost lidar for path mapping. SLAM is the algorithm widely used to localize the vehicles with the given sensor data. SLAM can help the robot to know where they are in the world. SLAM can also work properly even without IMU and GPS. Hence, another motivation is to employ SLAM algorithm on robot without IMU or places with no GPS (indoor). The last motivation is to use limited resources to prototype an AVs for project purposes.
The bot(model), which has simple structure uses ROS (Robotic Operating System) software library of version ROS melodic booted with Raspberry Pi and interfaced with RPLidar in the front top portion of the Bot. This low-cost mapping robot can emerge with features like SLAM (Simultaneous Localization and Mapping)which has the capability to form the Map of the environment using Lidar scans using MATLAB’s Robotic Operating System Software package to communicate with ROS in the Raspberry Pi using ROS Network Configurations.

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