一个基于视觉-惯性压力融合的水下同时定位和映射系统
Sensors (Basel, Switzerland)
|May 25, 2024
概括
检测水下物体是一个挑战. 一个新的ORB-SLAM3-VIP系统融合了深度,IMU和光学传感器,大大提高了用于海军地雷对策的自主导航精度.
科学领域:
- 机器人技术和自主系统
- 海洋学和海洋技术
- 计算机视觉和传感器融合
背景情况:
- 精确的海底物体检测对于海军地雷对策 (MCM) 至关重要,由于单传感器导航系统的局限性而受到阻碍.
- 现有的声纳技术 (侧扫描,合成光圈) 覆盖了大面积,但缺乏自主导航的精度.
研究的目的:
- 开发一个增强的同时定位和绘图 (SLAM) 系统,以改善水下导航和物体检测.
- 通过整合多个传感器模式来解决MCM操作中单传感器系统的精度限制.
主要方法:
- 拟议的ORB-SLAM3-VIP框架,ORB-SLAM3-VI的扩展,紧密的配对深度,惯性测量单元 (IMU) 和光学传感器.
- 实现了多传感器融合SLAM模型,将深度和IMU测量集成到视觉SLAM算法中.
- 在初始化,尺度微调,跟踪和映射中引入深度约束,以完善Z轴定位并提高姿势和尺度估计的准确性.
主要成果:
- 与ORB-SLAM3-VI.相比,ORB-SLAM3-VIP显示了AQUALOC数据集的显著改进,与ORB-SLAM3-VI.相比.
- 在所有测试的水下序列中,尺度误差减少了高达41.2%.
- 轨迹误差减少了高达41.2%,平方根减少了高达41.6%.
结论:
- ORB-SLAM3-VIP系统为水下航行提供了卓越的姿势和尺度估计精度.
- 多传感器融合,特别是在深度限制下,有效地克服了传统MCM传感器系统的局限性.
- 这种增强的SLAM方法为自主水下操作和物体检测提供了更精确的基础.
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