Data acquisition method and device used for unmanned vehicle

Abstract

The invention discloses a data acquisition method and device used for an unmanned vehicle. A concrete implementation method includes that selecting a laser radar of the highest importance level amongat least one laser radar of the unmanned vehicle as a first laser radar and acquiring a starting time point of the current time window and performing the following data processing steps: performing real time acquisition and storage operation on point cloud data packets collected by each laser radar among the at least one laser radar after the start time point of the current time window; determining any one condition in a condition set is met or not; setting the start time point of the current time window as the current time and performing the data processing steps continually in response to that said condition among the condition set is met. By adopting the implementation method, aligned storage of point cloud data collected by the at least one laser radar of the unmanned vehicle is realized.

Description

technical field [0001] The present application relates to the technical field of motor vehicles, in particular to the technical field of unmanned vehicles, and in particular, to a data acquisition method and device for unmanned vehicles. Background technique [0002] The control systems of driverless vehicles rely on sensors to sense their surroundings. In order to obtain more and more comprehensive information, driverless vehicles are usually equipped with various sensor devices. Among all sensors, lidar carries the most important functions such as vehicle positioning and obstacle detection. At present, different lidars are set up at different locations of the unmanned vehicle, and multiple lidars cover different viewing angles due to their different locations. In order to achieve better control effects, the control system of the driverless vehicle will fuse the data collected by multiple lidars. [0003] At present, the industry mainly adopts the following two fusion me...

AI Technical Summary

Problems solved by technology

However, due to the different ranges and angles collected by different lidars, the most complete information can only be obtained by merging them together, and processing them separately will cause some point cloud data output by lidars to contain too little eigenvalue information, which greatly reduces the The accuracy of the algorithm recognition

[0006] Therefore, the existing multi-channel lidar data fusion technology has the problem of ghosting caused by data out-of-sync or too little eigenvalue information and low algorithm recognition accuracy.

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