Robust 3D Planes Detection under Noisy Conditions Using Scaled Difference of Normals

Abstract

3D planes detection is an important task that has numerous applications in urban environments. However, current methods do not deal appropriately with the noise and quantization artifacts of low-cost sensors. In this paper, we present the Scaled Difference of Normals, a points filter that addresses these issues and is implemented on top of the Fast and Deterministic Planes Detection Based on Hough Transform. We evaluated its precision by comparing the detected planes coefficients with semi-automatically generated ground truth data and confirmed that when compared to state of the art methods, the proposed method is fast and has superior precision even in the presence of high noise levels, quantization artifacts and several variations in the points distribution caused by registration.