Asynchronous data fusion method and device, storage medium and electronic equipment
A technology of asynchronous data and data, applied in the fields of instruments, biological neural network models, character and pattern recognition, etc., can solve the problems of poor fusion effect, data mismatch, poor fusion effect, etc.
Active Publication Date: 2021-09-10
北京轻舟智航智能技术有限公司
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But at the same time, due to the asynchronous problem of this method, that is, the time stamp of the data collected by the two types of sensors is far away (more than 100ms), the data does not match and the fusion effect is not good.
This type of method is currently mostly used in some traditional multi-sensor fusion methods, such as the multi-sensor fusion method based on Kalman filter; moreover, the current asynchronous fusion method has poor fusion effect due to data mismatch
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[0050] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined. In the following, the embodiment of the present invention will be described in detail by taking laser radar and camera as examples. It should be pointed out that the present invention is not limited thereto, and other radars that are functionally equivalent or similar can also be used, as long as they can realize the present invention technical solutions.
[0051] For a method for asynchronous data fusion provided by an embodiment of the present invention, see figure 1 shown, including: ...
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The invention provides an asynchronous data fusion method and device, a storage medium and electronic equipment. The method comprises the steps: obtaining the data of a current frame of laser radar, and determining the three-dimensional features of the current frame of laser radar; determining a fusion hidden state of a previous frame, performing time sequence fusion processing according to the fusion hidden state of the previous frame and the laser radar three-dimensional features of the current frame, and generating a temporary hidden state of the current frame and an output result of the current frame; and obtaining current frame camera data, determining current frame camera three-dimensional features, and generating a current frame fusion hidden state according to the current frame camera three-dimensional features and the current frame temporary hidden state. According to the asynchronous data fusion method and device, the storage medium and the electronic equipment provided by the embodiment of the invention, the processing delay is low by adopting a mode of carrying out asynchronous fusion on the current frame of laser radar data and the previous frame of camera data; Meanwhile, during asynchronous fusion, time sequence fusion is performed in combination with the previous frame of laser radar data, so that fusion can be better performed in the current frame, and the fusion effect is good.
Description
technical field [0001] The present invention relates to the technical field of asynchronous data fusion, in particular to an asynchronous data fusion method, device, storage medium and electronic equipment. Background technique [0002] Multi-sensor fusion technology refers to the method of fusing the synchronized different sensor data. This technology has been widely used in a variety of robotic applications, such as autonomous driving, advanced driver assistance systems (ADAS), smart home, surgical robots, industrial control, etc. Traditional multi-sensor fusion technology mostly adopts the method of Kalman filter. In recent years, with the rise of deep learning technology, artificial neural network has been more and more applied in the direction of multi-sensor data fusion because of its strong recognition ability. The artificial neural network can well learn the correlations between different sensor data and eliminate the interference of non-target information through t...
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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/251G06V20/56G06V10/454G06V10/806G06F18/253Y02A90/10G06V20/46G06V10/7715G06V10/80G06F18/25
Inventor 张雨
Owner 北京轻舟智航智能技术有限公司