Dynamic obstacle tracking method based on sparse laser radar data

A technology of dynamic obstacles and lidar, which is applied to the re-radiation of electromagnetic waves, the use of re-radiation, measurement devices, etc., can solve the problems of sparse point cloud, small amount of data, and poor accuracy of processing results.

Active Publication Date: 2020-06-26
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

However, the two-dimensional laser radar collects a point cloud on a horizontal plane, and the amount of data is small, which leads to sparse point cloud, and the accurac

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  • Dynamic obstacle tracking method based on sparse laser radar data
  • Dynamic obstacle tracking method based on sparse laser radar data
  • Dynamic obstacle tracking method based on sparse laser radar data

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Embodiment Construction

[0082] The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0083] This embodiment provides a technical solution: a dynamic obstacle tracking method based on sparse lidar data, such as figure 1 shown, including the following steps:

[0084] Step One: Eliminate the Static Background

[0085] The data collected by lidar includes static obstacles and dynamic obstacles. For the tracking of dynamic obstacles, the static obstacle point cloud is noise, and this part of the point cloud must be removed. The general process of background elimination based on 3D radar is as follows: first determine the drivable area, then delete the ground point cloud and the point cloud exceeding a...

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Abstract

The invention discloses a dynamic obstacle tracking method and system based on sparse laser radar data, and belongs to the technical field of dynamic obstacle tracking, and the method comprises the following steps: S1, eliminating a static background; S2, performing dynamic point cloud clustering; S3, performing convex hull extraction; S4, performing feature extraction; S5, performing data association; S6: predicting a trajectory. According to the method, static obstacle laser radar point clouds are filtered by using a grid map, and extremely sparse point clouds are filtered by a small amountof residual static point clouds through a dbscan algorithm, so the final filtering effect is enhanced; point clouds are classified into ellipses, rectangles and straight lines in a fuzzy mode by extracting angle information of point cloud convex hulls, then correct point cloud position points are obtained by assisting the point clouds in fitting the sizes of graphs, and the accuracy of data association is guaranteed. In addition, the characteristics of the nearest neighborhood and the multi-target hypothesis algorithm are integrated, the multi-target association algorithm is improved, and dataassociation work can be efficiently completed on the premise of ensuring accuracy.

Description

technical field [0001] The invention relates to the technical field of dynamic obstacle tracking, in particular to a dynamic obstacle tracking method and system based on sparse laser radar data. Background technique [0002] Object detection and tracking technologies based on visual sensors are relatively mature, but visual sensors usually cannot provide distance information and must be used under good lighting conditions, which limits their use in robot navigation. Lidar is not affected by light and can provide distance data, which can complete tasks such as robot navigation and tracking. In recent years, using lidar to detect dynamic obstacles and predict their trajectory is a key topic in the field of mobile robotics. [0003] With the development of unmanned driving technology, there are already a large number of dynamic obstacle detection and tracking methods. For vehicle detection and tracking, Konrad M et al. use raster maps to detect and track vehicles. Chen Tongt...

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Application Information

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IPC IPC(8): G01S17/66G01S17/93G06K9/62
CPCG01S17/66G01S17/93G06F18/23G06F18/24
Inventor 周增祥孙翔峻段仕鹏左家乐黎梦涛柴源王应富刘志刚
Owner UNIV OF SCI & TECH OF CHINA
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