RWKV-VIO:使用端到端深度网络的高效和低漂移的视觉惯性计数
Jiaxi Yang1, Xiaoming Xu1, Zeyuan Xu2
1School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou 510275, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了RWKV-VIO,这是一种用于视觉惯性口径测量 (VIO) 的新框架,可以增强时间建模和计算效率. RWKV-VIO具有轻量级的设计,具有线性复杂性,可实现自主导航的高定位精度.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 视觉惯性计数 (VIO) 对于自主导航至关重要.
- 深度学习VIO方法在时间建模和计算效率方面存在困难.
- 像LSTM和变压器这样的现有模型在计算上昂贵,并且在处理时间尺度方面存在局限性.
研究的目的:
- 开发一个新的VIO框架,解决时间建模和计算效率的挑战.
- 介绍RWKV-VIO,一种轻量级且计算效率高的VIO解决方案.
- 改进特征提取和利用历史惯性数据.
主要方法:
- 根据RWKV架构提出的RWKV-VIO框架.
- 实现了一种具有线性计算复杂性的轻质结构.
- 开发了一种新的惯性测量单元 (IMU) 编码器,具有剩余连接和通道对齐.
- 采用了并行编码策略,使用两个独立初始化编码器进行多维特征提取.
主要成果:
- RWKV-VIO展示了卓越的计算效率和轻量级的设计.
- 与先进方法相比,显著减少模型大小和推断时间.
- 在公开共享的数据集上实现了排名最高的定位准确性.
结论:
- RWKV-VIO有效地解决了现有的深度学习VIO方法的局限性.
- 该框架为自主导航提供了计算效率高,准确的解决方案.
- 新的IMU编码器和并行编码策略增强了特征提取和模型性能.
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