Mobile robot positioning method based on occupancy grid map and combined with imu

Abstract

The invention relates to the technical field of robot autonomous navigation and specifically relates to a mobile robot positioning method based on an occupancy grid map and combined with an imu. The method comprises four parts, that is, pose prediction, feature extraction, data fusion and icp matching. The occupancy grid map is utilized to eliminate influence of a dynamic object so as to build a static map; and meanwhile, the imu is utilized to carry out pose prediction, and instead of simply using a pose transformation matrix predicated by the imu, a nonlinear least square method is established and an optimal pose transformation matrix is obtained by combining the pose predicated by the imu and the grid map. The method is a slam method with good robustness, and can obtain high-precision positioning within a certain period of time.

Description

Technical field [0001] The present invention relates to the technical field of autonomous robot navigation, and more specifically, to a mobile robot positioning method based on an occupancy grid map combined with imu. Background technique [0002] In modern unmanned vehicles, positioning is an essential part of technology. To realize automatic driving, or automatic navigation, unmanned vehicles need to obtain precise positioning to be able to navigate accurately. Just like a person walking, he must know his position and direction before he can know where he is going. There are many types of existing positioning technologies, including single-point GPS positioning, differential GPS positioning, lidar positioning, and computer vision methods. [0003] Each positioning technology has its own advantages and disadvantages. For example, single-point GPS relies on the number of satellites to measure the quality of positioning; differential GPS requires two stations to maintain decimeter...

AI Technical Summary

Problems solved by technology

[0003] Each positioning technology has its own advantages and disadvantages. For example, single-point GPS relies on the number of satellites to measure the quality of positioning; differential GPS requires two stations to maintain decimeter or even centimeter-level positioning. Generally speaking, base stations and mobile The stability of the sending and receiving signals of the station can guarantee the quality, and the transmission of the signals often brings about the increase of the technical cost; LiDAR positioning requires a large amount of calculation to calculate the point cloud matching, and the positioning effect is often not good; the positioning method of computer vision is very difficult. Depends on the quality of the camera, and the limitations of light, etc.

It is often not enough to rely on one technology alone to obtain a robust positioning effect, and it is necessary to fuse data from multiple sensor devices

Images