Point Cloud Simplification by Clustering for Robotics and Computer Vision Applications

This paper introduces new methods of point cloud simplification methods (reducing the complexity whilst preserving integrity), which are targeted at mobile robotics applications, where computational power is limited and online processing is beneficial. A review of existing point cloud simplification methods has been conducted which highlighted the existing methods focus on maintaining the accuracy of the point cloud rather than the computational requirements. The proposed algorithms presented are compared to known clustering algorithms intended for embedded application, and are evaluated based on the computational time required to generate a clustered point cloud, the quality of the resulting point cloud and its suitability to form a polygonal mesh. All algorithms were benchmarked on several popular single board computers (SBCs) as well as an x86 computer. The proposed algorithms are shown to have significantly better performance in terms of computational time, compared to existing methods, whilst attempting to maintain overall quality.