CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

TL;DR

This paper presents an optimized neural network algorithm for omnidirectional object detection on HermesBot, utilizing preliminary frame classification to enhance inference speed for autonomous delivery robots with multiple cameras.

Contribution

It introduces a novel optimization method combining preliminary frame classification with CNN-based object detection for omnidirectional robot perception.

Findings

Optimization accelerates inference time when up to 5 cameras detect objects.

The approach effectively reduces computational load in multi-camera setups.

Experimental results demonstrate improved processing efficiency for autonomous navigation.

Abstract

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.