Barrier detection and tracking method for port unmanned vehicle

Abstract

The invention discloses a low-cost barrier detection and tracking method for unmanned driving. The method comprises the following steps: acquiring environment point clouds, and overlapping the point clouds to obtain an environment point cloud set PC_0 of a current frame; removing points of which the height differences are larger than a threshold in a vehicle reference frame to obtain a point cloudset PC_1 consisting of non-ground points; filtering the length and width of the PC_1, keeping the environment point clouds within a fixed distance range, and converting the cloud points into a single-channel or three-channel feature image; inputting the obtained feature image into a built detection model to obtain an information list of a barrier target; building trackers for barrier targets of different types with different methods or parameters in combination with a barrier target information list of a previous frame according to the obtained barrier target information list, tracking each target in the current frame, and meanwhile calculating and updating motion information of the barrier targets; and repeating the steps 1 to 5 till the end of unmanned driving. The method has the advantages of low cost, high detection accuracy and high running speed.

Description

technical field [0001] The invention relates to the field of unmanned driving technology, in particular to an obstacle detection and tracking method for unmanned vehicles in ports. Background technique [0002] In recent years, with the popularization of various assisted driving systems and the emphasis on traffic safety from all walks of life, autonomous driving technology has developed rapidly. However, there is still a long way to go before fully self-driving cars for the public, and the high cost is the main factor hindering its progress. Under the existing environmental perception technology, vehicles need to be equipped with several different types of sensors such as cameras, lidars and millimeter-wave radars in order to obtain complete and reliable road information more accurately. [0003] Although the cost of a perception solution based on a monocular or multi-camera is low, the optical imaging of the camera is easily affected by many factors such as light source, ...

AI Technical Summary

Problems solved by technology

[0003] Although the cost of a perception solution based on a monocular or multi-eye camera is low, the optical imaging of the camera is easily affected by many factors such as light source, scene environment, and imaging field of view.

Moreover, the ranging accuracy of the camera attenuates sharply as the distance increases, making it impossible to obtain accurate obstacle distances

When millimeter-wave radar is used to detect obstacles, since the millimeter-wave may scatter and penetrate when it touches the target, it may sometimes cause false detection of the target and affect the detection accuracy of the entire perception system.

Although the price of lidar sensors has dropped in recent years, the price of some high-bundle lidars is still very high. For example, the unit price of Velodyne HDL-64E is as high as about 400,000 yuan, which is not suitable for the cost constraints of mass-produced models.

No reports of such techniques have been found so far

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