用惯性与动力数据学习水下机器人速度，实现无视觉长期定位。
DeepVL: Dynamics and Inertial Measurements-based Deep Velocity Learning for Underwater Odometry
- 结合惯性、电机指令和电池电压的递归网络预测速度与不确定性。
- 视觉缺失时相对位置误差低于4%，仅靠1个视觉特征时误差约2%。
- 适合水下机器人在无视觉环境下长时间稳定定位的场景。
本文提出一种基于动力学感知的自主感知方法，通过递归神经网络利用惯性信号、电机指令、电池电压及前一时刻隐状态，输出鲁棒的速度估计及其不确定性。采用网络集成提升预测性能，并将结果融合至扩展卡尔曼滤波器，结合惯性预测与压力计更新，实现无需外部感知的长期水下里程计。当集成至单目视觉-惯性里程计系统时，即使仅追踪1个视觉特征（较传统系统减少一个数量级），仍能显著增强估计鲁棒性。在实验室水池与特隆赫姆峡湾部署的水下机器人上验证，该方法在NVIDIA Orin AGX上推理时间不足5ms，完整视觉失效情况下新轨迹相对位置误差低于4%，最多保留2个视觉特征时相对误差约为2%。
原文摘要 · Abstract (English)
This paper presents a learned model to predict the robot-centric velocity of an underwater robot through dynamics-aware proprioception. The method exploits a recurrent neural network using as inputs inertial cues, motor commands, and battery voltage readings alongside the hidden state of the previous time-step to output robust velocity estimates and their associated uncertainty. An ensemble of networks is utilized to enhance the velocity and uncertainty predictions. Fusing the network's outputs into an Extended Kalman Filter, alongside inertial predictions and barometer updates, the method enables long-term underwater odometry without further exteroception. Furthermore, when integrated into visual-inertial odometry, the method assists in enhanced estimation resilience when dealing with an order of magnitude fewer total features tracked (as few as 1) as compared to conventional visual-inertial systems. Tested onboard an underwater robot deployed both in a laboratory pool and the Trondheim Fjord, the method takes less than 5ms for inference either on the CPU or the GPU of an NVIDIA Orin AGX and demonstrates less than 4% relative position error in novel trajectories during complete visual blackout, and approximately 2% relative error when a maximum of 2 visual features from a monocular camera are available.
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