LiDAR data compression
P. Rajalakshmi, Bhaskar Anand, Abhishek Thakur, Parvez Alam · 2025
LiDAR data compression is a crucial technique for efficiently managing and transmitting large volumes of three-dimensional point cloud data generated by LiDAR sensors. Due to the high density and unstructured nature of point clouds, compression techniques are necessary to optimize storage and enable real-time applications such as autonomous vehicles, environmental monitoring, and urban planning. This chapter explores both traditional and deep learning-based compression methods, including octree-based techniques, voxelization, predictive coding, and convolutional neural network-based autoencoders. Additionally, a low-latency LiDAR data streaming framework is introduced, highlighting its components—Sensor-Side Agent (SSA), Data Streaming Agent (DSA), and Visualization Agent (VA). The framework&s;s implementation using WebRTC and Firebase is analyzed, evaluating factors such as compression efficiency, transmission latency, and scalability. Experimental results demonstrate the effectiveness of compression algorithms in preserving point cloud quality while optimizing data transfer. The discussion provides insights into choosing the appropriate compression and transmission method based on application requirements, bandwidth constraints, and security considerations.