Octree map construction method and system

Abstract

The invention discloses an octree map construction method and system. The method comprises the following steps of determining a key frame and a mobile robot pose under the key frame by adopting a laser map optimization SLAM algorithm according to laser radar and odometer data, collecting environment point cloud information, and converting the point cloud data under a camera coordinate system into a world coordinate system, splicing the point cloud information after coordinate conversion to obtain a three-dimensional point cloud map, based on the three-dimensional point cloud map, conducting loopback detection and back-end optimization on a laser map optimization SLAM algorithm, and acquiring an updated mobile robot pose and an updated three-dimensional point cloud map, filtering the updated three-dimensional point cloud map, and removing outliers of the updated three-dimensional point cloud map, and converting the filtered three-dimensional point cloud map into an octree map. Compared with a point cloud map, the octree map constructed by the method is easy to store, smaller in occupied memory space, flexible and convenient, and can be directly used for navigation.

Description

technical field [0001] The invention relates to the technical field of three-dimensional map construction for mobile robots, in particular to an octree map construction method and system. Background technique [0002] With the requirements of application scenarios and tasks, 2D maps can no longer meet the task requirements, and 3D maps can describe the 3D space environment in detail. Therefore, there are more and more demands for 3D maps. The application of 3D maps is not only used for mobile robots. Positioning also requires other application requirements, such as 3D reconstruction, navigation, obstacle avoidance, etc. There are two main forms of three-dimensional maps: point cloud and octree. Among them, the point cloud map construction methods mainly include 3D laser SLAM, visual SLAM, and SLAM combining 2D laser and vision. Among them, visual SLAM is sensitive to light and environment, requires high computer computing power, and the calculation pose is not as accurate ...

AI Technical Summary

Problems solved by technology

Among them, visual SLAM is sensitive to light and environment, requires high computer computing power, and the calculation pose is not as accurate as laser

3D laser SLAM is costly and needs to match a large number of point clouds, which requires high computer performance

For example, the folds of curtains and other details that we don't need to be related to, it is a waste of space to put this kind of information in the map

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