Mobile autonomous robots include numerous sensors for environment perception. Cameras are an essential tool for robot's localization, navigation, and obstacle avoidance. To process a large flow of data from the sensors, it is necessary to optimize algorithms, or to utilize substantial computational power. In our work, we propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup. The obtained experimental results revealed that the proposed optimization accelerates the inference time of the neural network in the cases with up to 5 out of 6 cameras containing target objects.
@article{arxiv.2110.11829,
title = {CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification},
author = {Saian Protasov and Pavel Karpyshev and Ivan Kalinov and Pavel Kopanev and Nikita Mikhailovskiy and Alexander Sedunin and Dzmitry Tsetserukou},
journal= {arXiv preprint arXiv:2110.11829},
year = {2021}
}
Comments
Accepted to IEEE 20th International Conference on Advanced Robotics (ICAR) 2021, 6 pages, 7 figures