A Novel Real-Time LiDAR Data Streaming Framework

Abstract

Data transmission of familiar sensor data sources like video and audio is well explored. However, data acquired from the sensor emitting voluminous data cannot easily be transmitted due to a lack of bandwidth, which causes bottlenecks in the transmission. One valuable sensor in autonomous vehicles is light detection and ranging (LiDAR). In this article, a novel framework has been proposed, which is capable of efficiently streaming LiDAR data over the Internet. The proposed framework consists of various subsystems that handle data in multiple stages. On the sender side, the stages include data acquisition from the sensor, compression, encryption, and encoding of data, followed by transmission of encoded data over the Internet. On the receiver side, decoding, decryption, decompression, and data visualization are performed. The end-to-end framework runs over the robot operating system (ROS) platform. The data is acquired via ROS topics on the sender side and again published on ROS topics from the browser on the receiver side. This allows the users to apply any processing algorithm online with tolerable latency. The challenge of Clock Synchronization was addressed during the testing process. A detailed analysis to validate the quality of decompressed data has also been performed for which cloud distance was used as a metric. The implemented model was able to perform data transmission with an average latency of 160.62 ms for low-resolution compression mode.