Utilizing Autoencoders for Latent Representation and Efficient Transmission of LiDAR Data via LoRa in ROS

Abstract

This article presents a methodology for transmitting light detection and ranging (LiDAR) sensor data, which is frequently used in robots and autonomous vehicles, utilizing robot operation system (ROS) and LoRaWAN. A primary challenge is the limited bandwidth of the long range (LoRa) network, which restricts data transmission from high-volume sensors like LiDAR. To address this issue, we propose an edge processing technique that employs autoencoders to compress the LiDAR data into a latent representation for efficient transmission. Our method achieves an 82% reduction in the original LiDAR data size, allowing it to fit within constraints of LoRaWAN’s limited payload capacity. Additionally, our approach enables the reconstruction of the ROS topic on the receiver side, effectively extending the capability of ROS from local networks to wide-area networks (WANs). The results demonstrate the feasibility of LiDAR data transmission over LoRaWAN using our method, thereby supporting the deployment of LiDAR sensors in environments with constrained networks and limited mobile connectivity.