基于三维激光雷达和多视点深度图的静态目标分割技术系统设计

Yun-Hao Bai, Kuan-Yu Liao, You-Sheng Xiao, Yu-Chang Fan
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引用次数: 0

摘要

先进驾驶辅助系统(ADAS)和人工智能(AI)是近年来的热点问题,自动驾驶汽车在整个ADAS中占有重要地位。为了检测汽车周围的环境,传感器可能是敏感和即时的。激光雷达(光探测和测距)使用激光从周围物体获得反射率。对于点云中物体的聚类,点云的密度仍然很稀疏,使得聚类结果完整,我们实现了一个激光雷达与多视图图像相结合的系统,多视图生成的深度图像可以帮助我们清晰地聚类点云中的物体。此外,我们使用CORDIC(坐标旋转数字计算机)EEAS(扩展初级角度集)架构来解码从Velodyne HDL-64E收集的封装数据。利用数字芯片设计的流程,降低了功耗,加快了速度。该系统的精度为91.09%,处理时间为0.757 s。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
System Design for Static Objects Segmentation Technology Based on 3D LiDAR and Multi-View Depth Map
Advanced Driver Assistance System (ADAS) and Artificial Intelligent (AI) are the important issue in recent years, autonomous car plays an important role in whole ADAS. To detect the environment surround the car, the sensor might be sensitive and immediate. LiDAR (Light Detection and Ranging) uses Laser to get the reflectivity from the surrounding objects. For clustering the objects with point cloud, the density of the point cloud still sparse, making the cluster result completely, we implement a system combines LiDAR and multi-view image, the depth image is generated by multi-view can help us to cluster the object in point cloud clearly. In addition, we use CORDIC (Coordinate Rotation Digital Computer) EEAS (Extended Elementary Angle Set) architecture to decode the package data that collected from Velodyne HDL-64E. By using the flow of digital chip design, we reduce the power consumption and accelerate the speed. The proposed system achieves 91.09% accuracy and the processing time is 0.757 second.
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