Table of Links

I Introduction

II Method

III Datasets

IV Experiments, Acknowledgment, and References

IV. EXPERIMENTS

We test three model configurations on both the simulator and tank datasets. First, we implement a pose regression model based on GoogLeNet [7] as our baseline model. Secondly, we implement a model using ResNet-50 [9] to evaluate the improvement possible by use of a deeper network. ResNet allows us to have deeper networks with more free parameters, and tries to avoid the problem of overfitting by having residual connections between layers [9]. Thirdly, we implement a pose

regression model using an LSTM as an intermediate layer. The results indicate that all three configurations can perform well in both simulated and tank datasets as shown in Table I. The errors are minimal and of comparable magnitude to the noise in the pose recorded by the camera sensors. Fig. 4 shows that the trajectories predicted by the models are very close to the actual trajectories in terms of both position and orientation. With the simulator dataset, which is free of noise, turbidity, light distortion, and other challenges that may be observed in real underwater settings, the ResNet-50 and LSTM-based model performed better than the baseline model. On the other hand, with the tank datasets with some distortions typical of underwater scenarios, ResNet-50 performed worse than the baseline model, whereas the LSTM model did better than baseline with dataset #1 (which mostly featured translation but almost no rotation). This suggests the ResNet-50 architecture may be slightly overfitting despite the regularization, so it is not able to provide better performance in the tank dataset. We also note that data augmentation significantly improves the model performance, so in cases where there is no significant rotation, the use of data from both cameras can be used to bolster performance. Overall, these methods are robust for application in real underwater environments, and show promise for use in open water settings, where they will be tested next.

ACKNOWLEDGMENT

This research project is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre-Positioning (IAF-PP) Grant No. A20H8a0241.

REFERENCES

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