Point cloud registration state determination based on directional weighting

A controller, configured to execute program instructions to implement the following actions:

• obtaining an original point cloud and a target point cloud;
• registering the original point cloud and the target point cloud, and obtaining a HESSIAN matrix and a registered pose upon convergence of the registration;
• transforming the original point cloud based on the registered pose, to obtain a transformed point cloud;
• determining a first point cloud set based on the HESSIAN matrix and the transformed point cloud;
• determining a first weight value w1 of the first point cloud set based on a first eigenvalue and a second eigenvalue from the HESSIAN matrix, wherein the first eigenvalue is associated with a first direction and the second eigenvalue is associated with a second direction, wherein the first direction is perpendicular to the second direction;
• dividing the original point cloud into a second point cloud set and a third point cloud set;
• transforming the second point cloud set and the third point cloud set based on the registered pose;
• determining a second weight value w2 of the transformed second point cloud set and a third weight value w3 of the transformed third point cloud set;
• and
• determining a registration state of the original point cloud based on a distance between a point in the first point cloud set and a corresponding point in the target point cloud, a distance between a point in the transformed second point cloud set and a corresponding point in the target point cloud, a distance between a point in the transformed third point cloud set and a corresponding point in the target point cloud, the first weight value w1, the second weight value w2, and the third weight value w3.

A point cloud matching state determining method for an autonomous mobile machine, comprising:

• obtaining an original point cloud and a target point cloud;
• registering the original point cloud and the target point cloud, and obtaining a HESSIAN matrix and a registered pose upon convergence of the registration;
• transforming the original point cloud based on the registered pose, to obtain a transformed point cloud;
• determining a first point cloud set based on the HESSIAN matrix and the transformed point cloud;
• determining a first weight value w1 of the first point cloud set based on a first eigenvalue and a second eigenvalue from the HESSIAN matrix, wherein the first eigenvalue is associated with a first direction and the second eigenvalue is associated with a second direction, wherein the first direction is perpendicular to the second direction;
• dividing the original point cloud into a second point cloud set and a third point cloud set;
• transforming the second point cloud set and the third point cloud set based on the registered pose;
• determining a second weight value w2 of the transformed second point cloud set and a third weight value w3 of the transformed third point cloud set;
• and
• determining a registration state of the original point cloud based on a distance between a point in the first point cloud set and a corresponding point in the target point cloud, a distance between a point in the transformed second point cloud set and a corresponding point in the target point cloud, a distance between a point in the transformed third point cloud set and a corresponding point in the target point cloud, the first weight value w1, the second weight value w2, and the third weight value w3.