Automatic Label Injection Into Local Infrastructure LiDAR Point Cloud for Training Data Set Generation

Abstract

The representation of objects in LiDAR point clouds is changed as the height of the mounting position of sensor devices gets increased. Most of the available open datasets for training machine learning based object detectors are generated with vehicle top mounted sensors, thus the detectors trained on such datasets perform weaker when the sensor is observing the scene from a significantly higher viewpoint (e.g. infrastructure sensor). In this paper a novel Automatic Label Injection method is proposed to label the objects in the point cloud of the high-mounted infrastructure LiDAR sensor based on the output of a well performing "trainer" detector deployed at optimal height while considering the uncertainties caused by various factors described in detail throughout the paper. The proposed automatic labeling approach has been validated on a small scale sensor setup in a real-world traffic scenario where accurate differential GNSS reference data where also available for each test vehicle. Furthermore, the concept of a distributed multi-sensor system covering a larger area aimed for automatic dataset generation is also presented. It is shown that a machine learning based detector trained on differential GNSS-based training dataset performs very similarly to the detector retrained on a dataset generated by the proposed Automatic Label Injection technique. According to our results a significant increase in the maximum detection range can be achieved by retraining the detector on viewpoint specific data generated fully automatically by the proposed label injection technique compared to a detector trained on vehicle top mounted sensor data. INDEX TERMS object label,automatic label injection, roadside infrastructure sensors, training dataset I. INTRODUCTION

Setting Method of Downsampling Factor and Grid Factor for NDT Relocation Algorithm in Dynamic Environment

“…In order to obtain the maximum capacity of the algorithm for dynamic obstacles of different volumes, the size of voxel grid probability n can be specified. After obtaining the random voxel grid, the Gaussian noise is added to the obtained voxel grid [41]- [46]. The calculation method can be easily obtained by using the knowledge of normal distribution in probability theory.…”