Zhang Yanjun
Zhong Shan Polytechnic, Zhongshan, China.
PeerJ Comput Sci. 2023 Jun 16;9:e1426. doi: 10.7717/peerj-cs.1426. eCollection 2023.
Pedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension and high-resolution data could be obtained by building a three-dimensional point cloud. However, the point cloud data collected by laser radar is often massive and contains a lot of redundancy, which is not conducive to transmission and storage. So, the processing speed grows slow when the original point cloud data is used for recognition. On the other hand, the compression processing of many laser radar point clouds could save computing power and speed up the recognition processing.
The article utilizes the fusion point cloud data from laser radar to investigate the fast pedestrian recognition algorithm. The focus is to compress the collected point cloud data based on the boundary and feature value extraction and then use the point cloud pedestrian recognition algorithm based on image mapping to detect pedestrians. This article proposes a point cloud data compression method based on feature point extraction and reduced voxel grid.
The Karlsruhe Institute of Technology and Toyota Technological Institute data set is used to investigate the proposed algorithm experimentally. The outcomes indicate that the peak signal-to-noise ratio of the compression algorithm is improved by 6.02%. The recognition accuracy is improved by 16.93%, 17.2%, and 16.12%, corresponding to simple, medium, and difficult scenes, respectively, when compared with the point cloud pedestrian recognition method based on image mapping, which uses the random sampling method to compress the point cloud data.
The proposed method could achieve data compression better and ensure that many feature points are retained in the compressed Point Cloud Data (PCD). Thus, the compressed PCD achieves pedestrian recognition through an image-based mapping recognition algorithm.
行人识别具有重大的实用价值，是在自动驾驶中应用路径规划和智能避障的关键一步。近年来，激光雷达在无人驾驶的行人检测与识别中发挥了重要作用。通过构建三维点云可以获得更精确的高空间维度和高分辨率数据。然而，激光雷达采集的点云数据往往量很大且包含大量冗余信息，不利于传输和存储。因此，使用原始点云数据进行识别时处理速度会变慢。另一方面，对许多激光雷达点云进行压缩处理可以节省计算能力并加快识别处理速度。
本文利用激光雷达的融合点云数据来研究快速行人识别算法。重点是基于边界和特征值提取对采集到的点云数据进行压缩，然后使用基于图像映射的点云行人识别算法来检测行人。本文提出了一种基于特征点提取和简化体素网格的点云数据压缩方法。
使用卡尔斯鲁厄理工学院和丰田技术研究所的数据集对所提算法进行实验研究。结果表明，与基于随机采样方法压缩点云数据的基于图像映射的点云行人识别方法相比，压缩算法的峰值信噪比提高了6.02%。在简单、中等和困难场景下，识别准确率分别提高了16.93%、17.2%和16.12%。
所提方法能够更好地实现数据压缩，并确保在压缩后的点云数据（PCD）中保留许多特征点。因此，压缩后的PCD通过基于图像的映射识别算法实现行人识别。
