Robot and repositioning method thereof

Abstract

The invention discloses a robot and a repositioning method thereof. The method comprises the following steps of acquiring the pose increment of an endpoint of a laser radar scanning frame of the robotrelative to an origin in a radar coordinate system; traversing each pose with the known grid map, and obtaining the updated grid probability corresponding to the endpoint of the laser radar scanningframe under the current pose; and determining the pose corresponding to the updated grid probability with the largest correlation function response as a re-positioning result of the robot. According to the method disclosed by the invention, the problem of " snapping " of the robot is solved on the basis of the re-positioning realized by related scanning matching, the algorithm does not depend on the initial value, the global optimum value is obtained through global searching based on geometric departure, and the algorithm is simple and effective. After the robot is re-positioned, working suchas the SLAM and the like can be continued, and the task failure of the robot is avoided.

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...

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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

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