With the continuous development and application of artificial intelligence technology, lidar has become one of the most commonly used perception devices in the fields of autonomous driving and intelligent robots. However, in the lidar perception system, how to optimize the data acquisition, processing and analysis process to achieve more accurate target detection and recognition is an important research direction. This article will introduce the LiDAR perception system scheme based on artificial intelligence technology, including its main components and technical principles.
LiDAR perception system mainly includes three aspects: hardware equipment, data processing and artificial intelligence algorithms. Among them, the hardware device is a physical part of the lidar perception system, data processing is to clean, screen and merge the collected lidar data, and the artificial intelligence algorithm is to detect and identify the target of the processed lidar data and output the corresponding results.
In terms of hardware equipment, lidar is the core device of lidar perception system. It can detect the surrounding environment by emitting a laser beam, and calculate information such as the position, distance, and size of the target object based on information such as the time difference and intensity of the reflected light. At present, the commonly used lidar on the market includes solid-state lidar and rotary lidar. Among them, solid-state lidar has the advantages of small size, low power consumption and high accuracy, while rotary lidar can cover a wider range.
In terms of data processing, lidar perception systems need to clean, screen and merge the collected raw data. This is because, in practical applications, lidar may be affected by various factors such as weather and environment, resulting in unstable data quality or noise. Therefore, it is necessary to use filtering, clustering and other techniques to preprocess and optimize the data to improve the data quality and accuracy.
In terms of artificial intelligence algorithms, deep learning has become one of the most commonly used algorithms in lidar perception systems. The object detection and recognition algorithm based on deep learning usually adopts model structures such as convolutional neural network (CNN) and recurrent neural network (RNN), and is trained and optimized by backpropagation algorithm. By feeding lidar data into CNNs or RNNs for training and learning, functions such as detection, classification and recognition of target objects can be effectively realized.
In short, the lidar perception system scheme based on artificial intelligence technology has become a very important and widely used technology in the fields of automatic driving and intelligent robots. By combining hardware devices, data processing and artificial intelligence algorithms, high-precision and efficient object detection and recognition can be achieved, which provides strong support for automatic control and intelligent decision-making. In the future, with the continuous development and innovation of artificial intelligence technology, the research and application of LiDAR perception system will be further promoted and improved.