Voxel planes: Rapid visualization and meshification of point cloud ensembles

Abstract

Conversion of unorganized point clouds to surface reconstructions is increasingly required in the mobile robotics perception processing pipeline, particularly with the rapid adoption of RGB-D (color and depth) image sensors. Many contemporary methods stem from the work in the computer graphics community in order to handle the point clouds generated by tabletop scanners in a batch-like manner. The requirements for mobile robotics are different and include support for real-time processing, incremental update, localization, mapping, path planning, obstacle avoidance, ray-tracing, terrain traversability assessment, grasping/manipulation and visualization for effective human-robot interaction.We carry out a quantitative comparison of Greedy Projection and Marching cubes along with our voxel planes method. The execution speed, error, compression and visualization appearance of these are assessed. Our voxel planes approach first computes the PCA over the points inside a voxel, combining these PCA results across 2x2x2 voxel neighborhoods in a sliding window. Second, the smallest eigenvector and voxel centroid define a plane which is intersected with the voxel to reconstruct the surface patch (3-6 sided convex polygon) within that voxel. By nature of their construction these surface patches tessellate to produce a surface representation of the underlying points.In experiments on public datasets the voxel planes method is 3 times faster than marching cubes, offers 300 times better compression than Greedy Projection, 10 fold lower error than marching cubes whilst allowing incremental map updates.

Real-Time Compact Environment Representation for UAV Navigation

“…There is also some literature on the plane extraction algorithm [ 28 , 29 , 30 , 31 , 32 ]. This algorithm can segment the parts of the depth image or the point cloud data belonging to the same plane, thereby utilizing a small number of geometric parameters to simplify the environment representation.…”

“…This algorithm can segment the parts of the depth image or the point cloud data belonging to the same plane, thereby utilizing a small number of geometric parameters to simplify the environment representation. For example, approximate planes are extracted by principal component analysis (PCA) based on voxel maps, and the plane parts are preserved for path planning [ 28 ]. However, some irregular obstacle surfaces may be lost during the conversion process due to the preset roughness threshold [ 29 ].…”

3D Surface Reconstruction from Voxel-based Lidar Data

“…Other methods create local descriptions of the surface by a set of unoriented discs (-surfels-) calculated from the points distribution inside defined neighborhoods. Surfels have been used for traversability assessment [8], and more recently to perform simultaneous localization and mapping (SLAM) in outdoor urban environments [9]. Implicit methods: For other applications such as physical modeling or detailed terrain traversability, a more accurate and continuous representation might be preferred.…”

“…It has been shown in [8], [9], that complex environments are efficiently approximated as a set of primitive local surfaces. This is especially suitable for mobile robotics, as robots usually evolve in well structured environments.…”

“…5. Following [8], for a given neighborhood H (indices are dropped for clarity) we obtain the cardinal |H|, statistical mean H µ , and covariance H σ from the merging of statistical data of all voxels in H.…”