A System Architecture of a Fusion System for Multiple LiDARs Image Processing

Abstract

LiDAR sensors are extensively used in autonomous vehicles and their optimal use is a critical concern. In this paper, we propose an embedded software architecture for multiple LiDAR sensors, i.e., a fusion system that acts as an embedded system for processing data from multiple LiDAR sensors. The fusion system software comprises multiple clients and a single server. The client and server are connected through inter-process communication. Multiple clients create processes to process the data from each LiDAR sensor via a multiprocessing method. Our approach involves a scheduling method for efficient multiprocessing. The server uses multithreading to optimize the internal functions. For internal communication within the fusion system, multiple clients and a single server are connected using the socket method. In sequential processing, the response time increases in proportion to the number of connected LiDAR sensors. By contrast, in the proposed software architecture, the response time decreases in inverse proportion to the number of LiDAR sensors. As LiDAR sensors become increasingly popular in the field of autonomous driving, the results of this study can be expected to make a substantial contribution to technology development in this domain.

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

“…LiDAR is a key sensor in a perception system since its output can be used for improving autonomous driving tasks based on sensor fusion processing [ 59 ], e.g., object detection and segmentation for object classification and collision avoidance [ 23 , 24 , 25 ], Simultaneous Localization and Mapping (SLAM) applications [ 60 ], the detection and navigation of the drivable area [ 61 , 62 ], and much more. The LiDAR output is a point cloud, which is an exact 3D image of the environment captured by the sensor.…”

PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT

“…When a computer is equipped with a multi-core processor, the use of multi-threading techniques can simplify the programming structure, improve the utilization of the computer's central processing unit (CPU) resources, better use of the computer hardware advantages, and avoid the waste of hardware resources (Jung et al, 2022). By analyzing the structure of the algorithm in this paper, the feature extraction of two images by the multithreading technique is proposed.…”