CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification
This work addresses computational efficiency for autonomous delivery robots, but it is incremental as it builds on existing object detection methods with a specific optimization.
The paper tackled the problem of processing high data flow from multiple cameras on an autonomous robot by proposing a CNN-based object detection algorithm with preliminary binary frame classification, which accelerated inference time by up to 5 out of 6 cameras containing target objects.
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.