Abstract
Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of the voxels with the same size, which fairly suites parallel computation. Apache Spark is a popular distributed parallel computing platform which allows fault tolerance and memory cache. These features can significantly enhance the performance of Apache Spark and results in an efficient and robust implementation. In our experiments, both synthetic and real point cloud data are employed to demonstrate the quality of our method.
Highlights
The acquisition of point cloud data continuously becomes more convenient and more economical due to the great development of surveying technologies such as mobile mapping [Tao and Li, 2007]
We present a method using cloud computing technologies to address the problem of change detection in the context of big data
The two point clouds are generated by randomly sampling on two Gaussian surfaces with different peaks
Summary
The acquisition of point cloud data continuously becomes more convenient and more economical due to the great development of surveying technologies such as mobile mapping [Tao and Li, 2007]. Can mobile mapping deliver a low-priced acquisition with high density and accuracy, the entire process is much faster than traditional LiDAR and a big volume of data can be produced in a moment, e.g., up to 500,000 points per second [ABA Surveying, 2015]. Such large datasets can cause the classical data processing methods inadequate due to a high demand for computing resources, which encourages new approaches to fill the gap. We present a method using cloud computing technologies to address the problem of change detection in the context of big data.
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