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城市环境实例细分模型与数据融合

Kaiyue Du¹, Jin Meng¹, Xin Meng¹

¹School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

本研究提出了一个轻量级,高效和准确的3D城市环境实例分割模型,用于自动驾驶汽车. 它通过使用先进的算法将LiDAR和摄像头数据融合起来,达到99.3%的分类准确度.

科学领域:

计算机视觉计算机视觉
机器人技术机器人技术机器人技术
人工智能的人工智能

背景情况:

城市环境的细粒度实例细分对于自动驾驶汽车的感知至关重要.
现有的方法往往难以区分类似的对象,并且需要大量的计算资源.

研究的目的:

使用合并的LiDAR和摄像头数据开发一个强大的和高效的3D城市环境实例细分模型.
在复杂的城市场景中提高对象分类和细分的可靠性和准确性.

主要方法:

一种双重融合方法,结合LiDAR点云和摄像头图像的数据级和功能级融合.
整合马尔科夫随机场 (MRF) 以优化基于空间上下文的支持向量机 (SVM) 分类.
使用Mean Shift算法进行对象分类和实例细分.

主要成果:

环境对象的分类准确率达到了99.3%.
成功分割相同类型的单个对象,而不需要实例标签.
通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接,通过空间上下文链接.

结论:

拟议的双融合模型为3D城市环境实例细分提供了一个轻量级,高效和准确的解决方案.
该方法通过提高对象分类和细分可靠性来增强自动驾驶汽车的感知.
该模型的效率使其适用于自动驾驶系统的实时应用.

关键词:

马尔科夫随机场是一个随机场.支持矢量机器支持矢量机器数据融合数据融合环境感知环境感知实例细分实例细分实例细分平均值转移的转移意味着

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