Three-dimensional trajectory smoothing processing method, device and equipment and storage medium

Abstract

The invention discloses a three-dimensional trajectory smoothing processing method, which comprises the steps of calculating original trajectory energy of data points and constructing a correspondingmatrix P according to F-frame data information obtained by tracking a three-dimensional data trajectory of a target, and calculating an initial signal-to-noise ratio of the data points according to the original trajectory energy and initial noise trajectory energy; when the initial signal-to-noise ratio is greater than the threshold SNR, carrying out dimension reduction estimation smoothing processing on a target three-dimensional data track or data after final smoothing processing is output. The invention further provides a three-dimensional trajectory smoothing processing device and equipment and a computer readable storage medium, through the implementation, whether the signal-to-noise ratio in the smoothing processing process reaches the preset threshold SNR or not can be monitored inreal time, so that fine processing control over the data points is achieved, and therefore high-frequency information is prevented from being filtered out.

Description

technical field

[0001] The present invention relates to the field of three-dimensional technology, in particular to a three-dimensional trajectory smoothing processing method, device, equipment and computer-readable storage medium. Background technique

[0002] At present, 3D technology is used to construct scenes and even record the movement trajectories of some targets. However, due to noise interference, the collected data may have large fluctuations. In order to solve such The problem is that the noise reduction (that is, smoothing) processing of the collected data is commonly used at present, such as digital filtering methods and nonlinear optimization methods. However, digital filtering is easy to mistake the feature points of high-frequency information as noise and filter them out, such as , when the target object moves too fast and has a large range, it is often easy to lose these data information during the data denoising process, resulting in the loss of some impor...

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