FGO-PMB:一个因子图优化Poisson多伯努利过器,用于准确的在线3D多对象跟踪
Jingyi Jin1, Jindong Zhang1,2, Yiming Wang1
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了FGO-PMB,这是使用LiDAR数据进行强大的3D多对象跟踪 (3D MOT) 的新框架. 它通过将概率过与因子图优化统一用于稳定对象跟踪来增强自主系统中的感知.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 可能的机器人学概率机器人学
背景情况:
- 基于LiDAR的自主系统需要可靠的感知.
- 激光雷达数据带来了稀疏性,遮蔽性和噪声等挑战,影响了跟踪稳定性.
- 现有的方法在3D多对象跟踪 (3D MOT) 中面临不确定性和不稳定性.
研究的目的:
- 使用LiDAR开发一个统一的概率框架,用于使用强大的3DMOT.
- 解决基于LiDAR的对象跟踪中的不确定性和不稳定性问题.
- 为了提高自主系统的感知可靠性.
主要方法:
- 拟议的FGO-PMB:一个整合Poisson多伯努利 (PMB) 过器 (随机有限集理论) 与因子图优化 (FGO) 的框架.
- 制定对象状态,存在概率和关联权重作为因子图中可优化的变量.
- 定义了四个因素 (状态过渡,观察,存在,关联一致性) 来编码时空约束.
主要成果:
- 在LiDAR扫描中实现了时间一致和不确定性意识的估计.
- 在KITTI和nuScenes数据集上展示了具有竞争力的3D MOT准确性.
- 保持实时性能. 保持实时性能.
结论:
- 通过将RFS不确定性建模与FGO全球优化统一,FGO-PMB为基于LiDAR的3DMOT提供了一个强大的解决方案.
- 该框架有效地处理LiDAR数据挑战,提高对自主系统的认识.
- 该方法提供了准确和稳定的实时对象跟踪.
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