LiDAR Data and Visual SLAM Based Robot Navigation Using Deep Learning

Robots can perceive their environment, identify objects, and prevent collisions with the use of LiDAR (Light Detection and Ranging) technology. Navigating an unfamiliar terrain is one of an autonomous vehicle's main goals. Creating a map is one way to accomplish this goal. A map gives the car the ability to make its own routes between the locations it has visited in order to complete a mission. Obtaining such a map in situations when there is no prior information of the environment or even the robot's starting location inside it is a unique challenge. For mobile robots to function in the real world, they must solve the basic challenge of local navigation, which entails moving towards a target point while avoiding obstacles. Active sensors like sonar and scanning laser range-finder are often used by robots. On the other hand, we know from first-hand experience that local navigation by sight alone is feasible. Automating this process has proven to be somewhat challenging. The application of robots may provide an answer to issues in hazardous environments, protecting both people and the environment. The robot's precise location and the route free of obstacles are determined using a variety of sensors. Traditional sensors, on the other hand, have their limits when it comes to processing complexity, spatial resolution, and detecting distance. The control algorithm and data collecting from sensors are put into action. The mobile robot is able to steer clear of obstacles of varying sizes and colors. Even while moving about within a room, this mobile robot can avoid bumping against walls or other obstacles.