RPNet: Rotational pooling net for efficient Micro Aerial Vehicle trail navigation

Abstract

Smart agile Micro Aerial Vehicles are becoming ubiquitous in industrial applications; however, their computational efficacy poses a constraint limiting their deployment. The challenge mentioned above is mitigated in the context of a Micro Aerial Vehicle vision-based navigation task. RPNet, a computational-efficient model, is proposed as a Micro Aerial Vehicle navigational controller. RPNet comprises a sequential arrangement of generic imaginary and real Gabor filters for computation reduction. Further, a novel rotational pooling mechanism that induces online feature descriptors augmentation is proposed and plugged after each convolutional block in RPNet for robustness and increase in performance. RPNet is initially trained on synthetic data for domain knowledge and further trained and tested in a real-world setting using a Micro Aerial Vehicle. Extensive experimental verification of RPNet based on four evaluation metrics shows satisfactory performance compared to the reference trajectories. Further, via comparisons, RPNet attains better error distribution of about ±5 m, and computational conservation of around 9% than the first runner-up comparator among the state-of-the-art models in the vision-based Micro Aerial Vehicle navigation task.