A Semantic Communication System for Real-time 3D Reconstruction Tasks

TL;DR

This paper introduces a semantic communication system enabling real-time 3D semantic mapping on resource-limited mobile devices, combining cloud-edge computing, attention-based stability, and verified through simulations and real indoor tests.

Contribution

It proposes a novel encoding-decoding semantic communication framework with an attention mechanism for stable data transmission under limited resources and varying channel conditions.

Findings

Achieves less than 0.1% error in mapping accuracy.

Real-time mapping update time under 1 second.

Outperforms existing semantic communication algorithms in speed and channel adaptation.

Abstract

3D semantic maps have played an increasingly important role in high-precision robot localization and scene understanding. However, real-time construction of semantic maps requires mobile edge devices with extremely high computing power, which are expensive and limit the widespread application of semantic mapping. In order to address this limitation, inspired by cloud-edge collaborative computing and the high transmission efficiency of semantic communication, this paper proposes a method to achieve real-time semantic mapping tasks with limited-resource mobile devices. Specifically, we design an encoding-decoding semantic communication framework for real-time semantic mapping tasks under limited-resource situations. In addition, considering the impact of different channel conditions on communication, this paper designs a module based on the attention mechanism to achieve stable data transmission under various channel conditions. In terms of simulation experiments, based on the TUM dataset, it was verified that the system has an error of less than 0.1% compared to the groundtruth in mapping and localization accuracy and is superior to some novel semantic communication algorithms in real-time performance and channel adaptation. Besides, we implement a prototype system to verify the effectiveness of the proposed framework and designed module in real indoor scenarios. The results show that our system can complete real-time semantic mapping tasks for common indoor objects (chairs, computers, people, etc.) with a limited-resource device, and the mapping update time is less than 1 second.