Robot and its relocation method

Abstract

The invention discloses a robot and its repositioning method. The method includes: acquiring the pose increment of the end point of the laser radar scanning frame of the robot relative to the origin in the radar coordinate system; traversing each location in the known grid map Pose, the update grid probability corresponding to the endpoint of the lidar scanning frame under the current pose is obtained; the pose corresponding to the update grid probability that maximizes the response of the correlation function is determined as the relocation result of the robot. The invention realizes the solution of the robot "kidnapping" problem based on the relocation realized by correlation scan matching, the algorithm does not depend on the initial value, and obtains the global optimal value through the global search based on the geometry, and the algorithm is simple and effective. After the robot is relocated, it can continue to perform work such as SLAM, avoiding the task failure of the robot.

Description

technical field [0001] The invention relates to the field of robot positioning, in particular to a robot and a repositioning method for the robot. Background technique [0002] With the development of robotics, robots are becoming more and more common. The robot may be "kidnapped" during use, for example, the robot is being picked up, kicked or slipped. The above-mentioned "kidnapping" situation may cause the failure of the positioning of the robot itself. At this time, the robot needs to be repositioned. Relocalization is an important basis for robot intelligent navigation and environment exploration, and it is also one of the key technologies for mobile robots to achieve true and complete autonomy. [0003] In the prior art, non-linear optimal scan matching is used for robot relocation. The non-linear optimization scan matching is based on the minimum mismatch between the current frame radar data occupancy grid probability and the map occupancy grid probability, and sol...

AI Technical Summary

Problems solved by technology

[0004] However, nonlinear optimization requires an estimated initial value, and then searches for a local optimal value near the initial value. If the estimated initial value is not near the global optimal value, the algorithm will converge to a wrong value. It can be seen that the nonlinear optimization scan matching pair estimate The initial value is sensitive and not suitable for finding the global optimal problem

Images