Mobile robot pose estimation method and system based on multi-sensor tight coupling

Abstract

The invention discloses a mobile robot pose estimation method and system based on multi-sensor tight coupling, and belongs to the technical field of robot localization, and the method comprises the steps of carrying out the feature matching of a current frame RGB-D image collected by a camera and a previous frame RGB-D image, and calculating a visual re-projection error in the matching process; integrating the data measured by the IMU, and constructing an IMU integral residual error; extracting edge feature points and plane feature points from the point cloud collected by the laser radar, calculating the distance from the edge feature points to the edge line and the distance from the plane feature points to the plane, and constructing a laser point cloud geometric residual error; performing pose estimation by taking the minimum visual re-projection error, IMU integral residual error and laser point cloud geometric residual error as targets to obtain a local pose; and updating the laser point cloud map by using the local pose, and performing global optimization on the laser point cloud map to obtain a global pose. According to the invention, the positioning precision and robustness of the mobile robot in complex motion and complex environment are improved.

Description

technical field [0001] The invention belongs to the technical field of robot positioning, and more particularly relates to a mobile robot pose estimation method and system based on multi-sensor tight coupling. Background technique [0002] In the field of mobile robots, the fusion of one or more of visual sensors, IMUs, and lidars is often used as the actual SLAM application algorithm. The visual SLAM method using a camera (monocular, binocular or depth) as a single sensor is greatly affected by ambient lighting, and it is difficult to detect effective features in an environment with sparse textures, resulting in missing positioning. The laser SLAM method using lidar (2D or 3D) as a single sensor has a low measurement frequency, and the lidar has motion distortion that is difficult to correct when the motion speed changes greatly. The IMU sensor performs motion estimation based on acceleration and angular velocity integration, and can provide more accurate motion estimation...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a mobile robot pose estimation method and system based on multi-sensor tight coupling, thereby solving the problem of existing robot positioning technology in fast movement and different complex environments. Technical issues of low precision and poor robustness

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