人工智能
计算机科学
校准
软件部署
过程(计算)
深度学习
计算机视觉
机器人学
领域(数学)
人工神经网络
目标检测
分割
光学(聚焦)
激光雷达
转化(遗传学)
保险丝(电气)
机器学习
机器人
工程类
光学
纯数学
化学
数学
地质学
物理
生物化学
电气工程
操作系统
统计
基因
遥感
作者
Shan Wu,Amnir Hadachi,Damien Vivet,Yadu Prabhakar
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2021-11-02
卷期号:21 (24): 27779-27788
被引量:12
标识
DOI:10.1109/jsen.2021.3124788
摘要
The technological advancement of sensors and computational power has opened a new chapter in machine learning for robotics applications, especially in image classification, segmentation, object detection, and self-driving cars. One of the challenges among these applications is improving the systems perception reliability and accuracy through sensors fusion. Hence, the focus on using Stereo-cameras and LiDARs as a complement to its accurate distance measurement. However, the calibration process of the sensors is mandatory before deployment. Some may use the conventional methods, including checkerboards, specific pattern labels, or even human labeling, which is labor-intensive and repetitive as it involves doing the same calibration process every time before using. In this work, we have proposed NetCalib – an auto-calibration methodology based on a deep neural network. This research aims to utilize the power of machine learning to find the geometric transformation between stereo cameras and LiDAR automatically. From the experiments, our method manages to find the transformations from randomly sampled artificial errors and outperforms the linear optimization-based ICP algorithm. Furthermore, this research work is open-sourced to the community to fully use the advances of the methodology and initiate collaboration and innovation in this field.
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