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KOM-SLAM:基于GNN的紧密结合的SLAM和多对象跟踪框架
Jinze Liu1, Ye Tian2, Yanlei Gu3
1Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-0033, Japan.
KOM-SLAM集成了同时定位和映射 (SLAM) 与使用图形神经网络 (GNN) 的多对象跟踪. 这种方法通过联合学习关键点和对象的协会来提高动态场景中的稳定性.
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
- 人工智能的人工智能
背景情况:
- 同时定位和映射 (SLAM) 和多对象跟踪对于自主系统至关重要.
- 现有的方法经常单独处理关键点和对象关联,从而限制了动态环境中的性能.
研究的目的:
- 开发一个紧密结合的SLAM和多对象跟踪框架,以在复杂的动态场景中提高稳定性.
- 在一个统一的框架内,共同学习跨框架的关键点和对象关联.
主要方法:
- 提出KOM-SLAM,这是一个使用图形神经网络 (GNN) 进行集成SLAM和多对象跟踪的新框架.
- 为关键点和对象关联构建了一个时空图,并结合了多层感知子 (MLP) 来实现自适应值.
- 实现了可差异化姿势估计的软赋值,允许通过姿势损失直接监督关联学习.
主要成果:
- 在KITTI跟踪基准上表现有所改善.
- 与现有方法相比,在本地化准确性和对象跟踪能力方面取得了卓越的结果.
- 在动态场景中展示了关键点和对象关联的联合学习的有效性.
结论:
- KOM-SLAM 提供了一种强大而有效的解决方案,用于合SLAM和多对象跟踪.
- 基于GNN的方法成功地解决了动态场景中单独的关联策略的局限性.
- 该框架可以通过可差分的姿势估计来直接监督关联学习,从而提高整体系统性能.
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