MA-EVIO:基于事件的视觉惯性耳度计的运动意识方法
Mohsen Shahraki1, Ahmed Elamin1, Ahmed El-Rabbany1
1Department of Civil Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种基于运动意识的基于事件的视觉惯性测距 (MA-EVIO) 系统,用于强大的室内定位. 通过基于运动的传感器数据的自适应融合,MA-EVIO在具有挑战性的条件下提高了准确性.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 同时定位和绘制 (SLAM)
背景情况:
- 全球导航卫星系统 (GNSS) 信号在室内不可靠,需要使用其他定位方法.
- 视觉惯性口径测量 (VIO) 融合了摄像头和惯性数据来估计姿势,但在高速运动和不良照明下会退化.
- 挑战包括运动模糊,传感器噪声和低时间分辨率,影响VIO的准确性和稳定性.
研究的目的:
- 开发一个强大而准确的室内定位系统,克服动态环境中的VIO限制.
- 提出一种基于运动感知事件的VIO (MA-EVIO) 系统,用于自适应式传感器融合和姿势估计.
主要方法:
- 实施混合追踪策略,结合稀疏特征匹配和直接光度对齐.
- 引入了基于实时运动分类和特征质量调整参数的运动感知关键选择.
- 开发了自适应式传感器融合,在快速运动中优先考虑事件数据,在慢运动中跟踪RGB/特征.
主要成果:
- 与最先进的方法相比,MA-EVIO在DAVIS240c和VECtor基准测试中表现优越.
- 在DAVIS240c上达到0.19的较低平均位置误差 (MPE),在VECtor上达到1.19%/1.28 deg/m的MPE/MRE.
- 在充满挑战的动态室内环境中表现优于EVI-SAM和PL-EVIO.
结论:
- 拟议的MA-EVIO系统有效地提高了室内定位的准确性和稳定性.
- 适应性传感器融合和运动感知关键选择对于处理动态环境至关重要.
- 在GNSS无法使用的情况下,MA-EVIO提供了一个有前途的解决方案,用于可靠的姿势估计.
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