Mobile robot positioning method

Abstract

The invention discloses a mobile robot positioning method, which belongs to the technical field of mobile robot positioning, and comprises the steps of placing a robot carrying a two-dimensional laserradar in a current positioning environment, and obtaining map point cloud of an obstacle; estimating the current pose of the robot by adopting a self-adaptive Monte Carlo positioning algorithm; converting the real-time data of the two-dimensional laser radar into point cloud PTCloud under the same coordinate system as the map point cloud by using the current pose of the robot; taking each frame of point cloud PTCloudscan as an input of an iterative closest point to obtain a rotation matrix R and a translation matrix T of the current frame of point cloud PTCloudscan relative to the map point cloud; and calculating the final pose of the robot according to the rotation matrix R and the translation matrix T. The pose obtained by adopting the adaptive Monte Carlo positioning algorithm is usedas the reference value to transform the point cloud coordinate system of the two-dimensional laser radar, so that the point cloud coordinate system is close enough to the current map point cloud, thematching speed of the iterative closest point is accelerated, and the positioning precision is improved.

Description

technical field [0001] The invention relates to the technical field of mobile robot positioning, in particular to a mobile robot positioning method. Background technique [0002] With the rapid development of science and technology and the advent of the era of artificial intelligence, mobile robots have developed rapidly and have been widely used in various industries. Among them, how to let the robot know its own position in the current environment and also feedback the position during the movement requires the use of positioning technology. When most robots work, high-precision positioning is first required to perform subsequent tasks. Therefore, how to position the robot in real time, accurately and efficiently becomes the key to the problem. [0003] The existing positioning technology is based on Kalman filtering (KF), which is an algorithm that uses the linear system state equation to optimally estimate the system state through the input and output observation data of...

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Problems solved by technology

[0004] However, due to the cumulative error of the odometer and the interference of different noises in the Kalman filter positioning method, the error will increase with time during the positioning process.

The adaptive Monte Carlo positioning requires that although the Kalman filter algorithm is relatively stable, its overall positioning accuracy is not high, and a large number of particles are required to maintain a high positioning accuracy.

The positioning method of using ICP to solve the relationship between the map and the current laser point will require a lot of calculations, especially in large maps where there are many point clouds, and the positioning time takes a long time. It cannot be time-sensitive during the robot's movement and cannot be applied to subsequent navigation. Function

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