Lecture 8: Visual Perception and Obstacle Avoidance Decision-making
Visual perception is the process in which a machine obtains environmental information through sensors and uses computer vision technology to analyze and understand the images. Including tasks such as object detection and object recognition, it provides the system with cognitive capabilities for the environment. Obstacle avoidance decision-making is based on the environmental information obtained by visual perception, and through environmental modeling, path planning and intelligent decision-making algorithms, behavioral strategies that can avoid obstacles, avoid collisions and achieve predetermined goals are formulated. The two are interdependent, and visual perception provides environmental data for obstacle avoidance decisions, and obstacle avoidance decisions perform behaviors based on these data. They play a key role in autonomous driving, robot navigation and other fields, promoting the intelligent application and development of unmanned systems in complex environments. RflySim allows the simulation of a variety of sensors, such as cameras and lidar, providing users with a platform to test visual perception algorithms. Users can create virtual environments, including buildings, terrain, and obstacles in RflySim to test and evaluate the performance of visual perception algorithms under different environmental conditions. With RflySim, developers can validate and optimize computer vision algorithms, such as object detection and object recognition, to meet the perception needs of drones in different scenarios. RflySim allows users to simulate path planning to test the drone's ability to choose a safe path and avoid obstacles in a virtual environment. Simulation developers can validate and adjust obstacle avoidance decision algorithms to ensure that drones can make intelligent decisions in the face of different environments and obstacle configurations.