The applications of robust estimation method BaySAC in indoor point cloud processing

Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.

Measurement of the banana pseudo-stem phenotypic parameters based on ellipse model

The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas,and it can provide essential data support for mechanized cultivation operations such as fertilization and pesticide application.Existing studies mainly measure the diameter of banana pseudo-stem as its phenotypic parameter.The banana pseudo-stem cross section was closer to an ellipse other than a standard circle,so the diameter parameter cannot adequately represent the phenotypic characteristics of the banana plant.In this study,an automatic measuring device for banana pseudo-stem phenotypic parameters was developed.The device,which integrates three different types of sensors:a laser ranging sensor,a rotary encoder,and a digital camera,were used to obtain the point cloud and image data of banana pseudo-stem.A K-means point clouds clustering algorithm based on Euclidean distance was proposed.The point cloud of banana pseudo-stem was identified and extracted.A three-dimensional reconstruction algorithm based on the ellipse model was also proposed.The three-dimensional contour of the pseudo-stem was calculated to obtain three types of phenotypic parameters:the long axis length,the short axis length,and the perimeter.Further,a synchronous trigger image acquisition mechanism was used to take pictures of pseudo-stems during measurement.It can be utilized for manual assessment of the growth status of the banana.Field experimental results showed that the three banana phenotypic parameters had a high correlation with the manual measurement results,and R^(2)is always more significant than 0.95,the total average measurement error and relative error were only 6.16 mm and 4.38%,respectively,both are within the acceptable agronomy range.In general,this method has good universality for plant stem detection,and the stem phenotypic parameters can be obtained by means of non-contact test,which is of great significance to the mechanized cultivation of the forest and fruit industry.