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Particle filtering (PF) algorithm has found an increasingly wide utilization in many fields at present, especially in nonlinear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. This article proposed an improved PF algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in Matlab, it discovers that the proposed algorithm provides better accuracy than sequential importance resampling, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filters in Gaussian mixture noise.

Stacked objects volume measurement is now widely used in the fields of enterprise material management. It is significant to improve the efficiency of enterprise management and to reduce the cost of management and operation. The method based on laser sensing is one of the key methods to measure the stacked objects volume. This paper presents a laser sensing measurement method of stacked objects based on bottom plane extraction and real-time calibration. A calibration method for a laser scanning sensor and inertial measurement sensor is proposed. Three-dimensional reconstructions of stacked objects and volume calculations are carried out after acquisition and processing of point clouds. Volume measurement experiments of the single box and stacked boxes have been conducted respectively. Experimental results show that the measurement method is feasible and valid with good accuracy.

Particle filtering algorithm has found an increasingly wide utilization in many fields at present, especially in non-linear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. The estimation process of particle filtering algorithm is a series of weighted calculation processes, which can be regarded as weighted data fusion. This article proposed an improved particle filtering algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in MATLAB, it discovers that the proposed algorithm provides better accuracy in comparison with particle filtering, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filter in Gaussian mixture noise. A practical seven-dimensional harmonic model is also implemented in the simulation. After comparing the performances of different algorithms, we found that the proposed method had more accuracy than the widely used extended Kalman filtering algorithm.