A Real-Time Multi-Model Parametric Representation of Point Clouds
This work addresses the problem of real-time, memory-efficient mapping for applications like multi-robot collaboration, though it is incremental as it builds on existing methods like Gaussian mixture models and B-spline surfaces.
The paper tackles the trade-off between computational efficiency and accuracy in parametric representations of point clouds by proposing a multi-model approach that combines Gaussian mixture models for segmentation with plane and B-spline surface fitting, achieving a 3.78 times efficiency improvement and 2-fold accuracy gain over baselines.
In recent years, parametric representations of point clouds have been widely applied in tasks such as memory-efficient mapping and multi-robot collaboration. Highly adaptive models, like spline surfaces or quadrics, are computationally expensive in detection or fitting. In contrast, real-time methods, such as Gaussian mixture models or planes, have low degrees of freedom, making high accuracy with few primitives difficult. To tackle this problem, a multi-model parametric representation with real-time surface detection and fitting is proposed. Specifically, the Gaussian mixture model is first employed to segment the point cloud into multiple clusters. Then, flat clusters are selected and merged into planes or curved surfaces. Planes can be easily fitted and delimited by a 2D voxel-based boundary description method. Surfaces with curvature are fitted by B-spline surfaces and the same boundary description method is employed. Through evaluations on multiple public datasets, the proposed surface detection exhibits greater robustness than the state-of-the-art approach, with 3.78 times improvement in efficiency. Meanwhile, this representation achieves a 2-fold gain in accuracy over Gaussian mixture models, operating at 36.4 fps on a low-power onboard computer.