Fitting primitive shapes to point cloud scenes is a challenging but neces-sary step for many robotic manipulation operations. State-of-the-art primitive fitting methods rely on geometric shape estimation or iterative procedures. They are often computationally complex and sensitive to al-gorithm parameterisation. This study tackles primitive fitting as a param-eter optimisation problem, solving it using the Bees Algorithm. The per-formance of the Bees Algorithm is evaluated on three sets of artificial scenes of varying degrees of blurriness and benchmarked against an evolutionary algorithm. Experimental results proved the precision and consistency of the Bees Algorithm. Primitive fitting times were compati-ble with real-time application.
|Title of host publication||Intelligent Manufacturing and Production Optimisation – The Bees Algorithm Approach|
|Editors||Duc Truong Pham, Natalia Hartono|
|Publication status||Published - 2022|
|Name||Edit Springer Series in Advanced Manufacturing|