Study of a Multi-Beam LiDAR Perception Assessment Model for Real-Time Autonomous Driving

Abstract

Light detection and ranging (LiDAR) provides a 3-D understanding of environment and plays an important role in autonomous driving. To study the influence of 3-D data quality on the environment perception and provide a theoretical basis for optimizing system design, a multi-beam LiDAR perception assessment model has been established to reveal the relationship between data quality and multi-parameters, including system and motion parameters. A novel ground segmentation algorithm was proposed with a combination of the grid elevation and the neighbor relationship, which was used to validate how the data quality influences the results of environment perception. By the way of down-sampling based on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, the experimental results showed that the proposed ground segmentation with combination of grid-elevation and neighbor-relationship (GSCGN) method was superior than other general ground segmentation methods in terms of accuracy and efficiency. It should be noted that the mean vertical angular resolution (MVAR), laser repetition frequency, and beam numbers were the dominant influencing parameters on the point density and the accuracy of ground segmentation. Based on the experimental results, the lower limits of system parameters were determined as 16-beam and 4-kHz repetition frequency, with the acceptable recall of 92.2% for ground and 93.5% for object, the accuracy of 92.9% and the runtime of 0.036 s, which can not only provide a reliable environment perception effect, but also reducing the computational burden to satisfy the real-time autonomous driving. This study offers a meaningful investigation to guide LiDAR system design with balancing the contradiction between the optimized system design and the high-degree environment perception.