Unsupervised Stream Learning for 3D Lidar Point Clouds

• Main Authors: C R, Shreelakshmi, Durbha, Surya S, Shinde, Rajat C, Talreja, Pratyush V, Singh, Gaganpreet
• Format: Conference Proceeding
• Language: English
• Subjects: (near) real-time analysis; 3D LiDAR point clouds; clustering; Clustering algorithms; Forestry; Laser radar; Point cloud compression; Prediction algorithms; Real-time systems; Three-dimensional displays; unsupervised stream mining
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS52108.2023.10281812

Light Detection and Ranging (LiDAR) is an active remote sensing technique that uses pulsed lasers to sense the surrounding environment. It works on the principle of Time of Travel (ToT) for acquiring highly accurate and high spatial resolution 3D information about the surrounding environment. LiDAR data possesses immense potential for (near) real-time applications (e.g., forestry, disaster, border security, etc.) Real-time applications demand quick analysis of the data and cannot wait for the entire data pertaining to the area of interest to be captured before producing useful insights. Thus, 3D LiDAR points should be processed as and when they are captured in the form of a continuous stream. Due to the lack of prior knowledge about the (near) real-time data and the underlying distribution, in this work, unsupervised stream mining approaches have been adapted for the analysis of streaming geospatial 3D LiDAR data. By applying different unsupervised data mining (clustering) algorithms on a huge set of 3D LiDAR data points, we have evaluated the quality of clustering based on different evaluation metrics.