A method to reduce the memory required for laser slam mapping

Abstract

The invention discloses a method for reducing the memory required for laser slam mapping. In the method, the member variables of each cell are stored separately, the members used in large quantities are kept in the cell, and the members with a relatively small amount of use are separated and saved. In the independent storage space, the invention is scientific and reasonable, safe and convenient to use. This method can not only save a lot of memory resources when building an environmental map with laser slam, but also has no effect on the accuracy of the map construction, and can ensure that the speed of map construction is basically the same. changes, the memory usage of this method is continuously increasing slowly, and there will be no sudden increase in memory usage, so the program runs smoothly, ensuring a more harmonious and stable system operation with other programs in the system, and, using This method makes the raster map have less impact on map accuracy.

Description

technical field [0001] The invention relates to the technical field of mobile robot positioning and mapping software algorithms, in particular to a method for reducing the memory required for laser slam mapping. Background technique [0002] SLAM, the full name is Simultaneous Localization and Mapping, which is called Simultaneous Localization and Mapping in Chinese. The SLAM problem can be described as: the robot starts to move from an unknown position in an unknown environment. Accurately estimate according to the map, and build an incremental map on the basis of its own positioning at the same time, to realize the autonomous positioning and navigation of the robot. The laser slam is called laser slam, and the visual slam is called visual slam, which is realized by the camera. The present invention The method is optimized for laser slam proposals. [0003] In some methods, each cell only has int type visits and n members when building a map. When the laser beam end hits t...

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Problems solved by technology

[0003] In some methods, each cell only has int type visits and n members when building a map. When the laser beam end hits the cell, it does not accumulate and save the laser end obstacle coordinates. According to the probability calculation, when the corresponding cell is an obstacle, The default obstacle position is at the geometric center of the cell, which is a rough method of estimating obstacles. During mapping and matching, the uncertainty of the estimated robot pose error increases, reducing the accuracy of mapping, making the overall The effect of mapping becomes worse, so people urgently need a method to reduce the memory required for laser slam mapping to solve the above problems

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