Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites

TL;DR

This paper introduces a lightweight deep neural network architecture designed for real-time scene segmentation on embedded platforms, enabling autonomous robots to navigate construction sites efficiently with limited computational resources.

Contribution

The paper proposes a novel, efficient neural network model specifically optimized for real-time semantic segmentation on resource-constrained embedded systems for autonomous navigation.

Findings

Model achieves real-time segmentation on embedded hardware

Outperforms existing models in efficiency and speed

Enables autonomous robots to navigate construction sites effectively

Abstract

Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules (e.g., context-awareness, control, localization, and mapping) on an embedded platform. Pixel-wise semantic segmentation provides a UV with the ability to be contextually aware of its surrounding environment. However, in the case of mobile robotic systems with limited computing resources, the large size of the segmentation model and high memory usage requires high computing resources, which a major challenge for mobile UVs (e.g., a small-scale vehicle with limited payload and space). To overcome this challenge, this paper presents a light and efficient deep neural network architecture to run on an embedded platform in real-time. The proposed model segments navigable space on an image sequence (i.e., a video stream), which is essential for an autonomous vehicle that is based on machine vision. The results demonstrate the performance efficiency of the proposed architecture compared to the existing models and suggest possible improvements that could make the model even more efficient, which is necessary for the future development of the autonomous robotics systems.