Fuzzy reasoning based motion estimation from range images

Many methods to estimate rigid body motion parameters from range images have been put forward in the last decade. Such methods work well for range image data corrupted by Gaussian random noise without outliers. In particular, the constraint least squares (CLS) is the most accurate, robust, stable, and efficient motion estimation algorithm. However, the CLS and none of the current methods are very robust in the presence of outliers. Therefore, in this paper, we focus on the problem of estimating motion parameters from noise and outlier corrupted range image data. We propose a novel motion estimation geometric algorithm with fuzzy reasoning (GAFR). The algorithm is based on the geometric properties of correspondence vectors to synthesise motion parameter candidates and employs a robust fuzzy reasoning method based on computing deviations and selecting estimates from membership function values. The GAFR is validated through experimentation using synthetic and real range image data