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End-to-end point cloud data compression method based on three-dimensional laser radar sensor

A radar sensor, three-dimensional laser technology, applied in neural learning methods, instruments, electromagnetic wave re-radiation, etc., can solve the problem of insufficient network transmission capacity and multi-sensor data, and achieve faster convergence speed, high precision, and high-efficiency fitting performance Effect

Active Publication Date: 2021-08-06
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The problem that comes with the above advantages is: conventional multi-line 3D lidar can provide up to millions of point cloud data
However, the network transmission capacity is far from enough to directly transmit multi-sensor data, especially for 3D lidar

Method used

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  • End-to-end point cloud data compression method based on three-dimensional laser radar sensor
  • End-to-end point cloud data compression method based on three-dimensional laser radar sensor
  • End-to-end point cloud data compression method based on three-dimensional laser radar sensor

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0053] First, our method is evaluated by three evaluation metrics, namely Mean Squared Error (RMSE), Structural Similarity, (SSIM) and Peak Signal-to-Noise Ratio. RMSE can reflect the numerical deviation between the compressed point cloud and the original point cloud. SSIM is used to describe the structural similarity between the compressed point cloud and the original point cloud. PSNR can be used to indicate the quality of compressed point clouds. We use more than 5000 frames of point cloud data from different dataset validation sets for evaluation experiments, including four representative scenes of campus, city, highway and village. See Table 1 for details.

[0054] Table 1: Data information in multiple scenarios

[0055]

[0056] This example compares the point cloud compression results of Google's Draco, Octree, JPEG-based methods and our method. For each method, we evaluate different compression levels in four cases and choose the lowest bits per point (Bpp) with...

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Abstract

The invention relates to the technical field of laser radar sensors and automatic driving, in particular to an end-to-end point cloud data compression method based on a three-dimensional laser radar sensor. The method combines an encoder with three-channel fusion neighborhood curvature and density characteristics and a robust decoder with additional trainable parameters. In combination with the feature information, the ConvLSTM can obtain more detail enhancement in the point cloud coding and decoding process. The structure of alternate decoding and up-sampling ensures the accurate recovery of the point cloud. In addition, the provided mixed loss function has higher convergence speed and better fitting performance. Experiments show that compared with a compression algorithm based on Draco, an octree and JPEG, the method can obtain a higher compression rate and satisfactory compression quality. In addition, the method provided by the invention has good generalization ability in different scenes.

Description

technical field [0001] The present invention relates to the technical fields of laser radar sensors and automatic driving, and more specifically, relates to an end-to-end point cloud data compression method based on a three-dimensional laser radar sensor. Background technique [0002] In recent years, unmanned vehicles have developed rapidly. As an effective solution for unmanned driving, scanning imaging lidar has attracted countries all over the world to actively carry out research on vehicle lidar. As one of the important sensors in unmanned driving technology, on-board lidar is of great significance to ensure the driving safety of unmanned vehicles. With the further development of the unmanned driving industry, the automotive lidar market has broad prospects. [0003] Among them, 3D lidar is an active remote sensing device based on photoelectric detection, which can obtain wider field of view and more direct 3D environmental information. At present, 3D lidar has been w...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S17/931G01S7/48G06N3/04G06N3/08
CPCG01S17/931G01S7/48G06N3/08G06N3/044G06N3/045Y02A90/10
Inventor 黄凯崔明月吴成昊刘云超王博罗宇翔
Owner SUN YAT SEN UNIV
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