Simulated data is increasingly popular for training and deploying deep learning models. Advances in simulation technology enable generating realistic synthetic data for applications such as robotics, autonomous vehicles, and computer vision. Although simulated data is cost-effective and efficient, deploying models in the real world requires careful consideration of transferability and generalization.

Autonomous mobile robot (AMR) navigation relies heavily on visual SLAM, which stands for visual simultaneous localization and mapping. This technique uses a camera to estimate the robot’s position while simultaneously creating a map of the environment. Visual SLAM is crucial for Mars exploration devices, unmanned robots, endoscopy, and vacuum cleaning.

ROS is an open-source framework that simplifies the development of complex robotic applications. It utilizes a distributed architecture that facilitates communication between various software components within a robot’s system.