Coordinate gradient method for point cloud registration for autonomous vehicles

A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations to register points clouds for an autonomous driving vehicles (ADV), the operations comprising:

• determining a frame pair size of one or more pairs of related poses of a plurality of poses of a plurality of point clouds of a region to be mapped;
• separating the region containing the plurality of point clouds and the corresponding poses into a plurality of clusters in response to the frame pair size being greater than a threshold;
• applying separately a first optimization model to the plurality of point clouds corresponding to the plurality of clusters to register the point clouds;
• merging the registered point clouds from the plurality of clusters to perceive a driving environment of the region surrounding the ADV;
• and
• repeatedly separating the registered point clouds from the plurality of clusters into an additional plurality of clusters and applying the first optimization model to the plurality of point clouds corresponding to the additional plurality of clusters until the registered point clouds converge.

A data processing system, comprising:

• a processor;
• and
• a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations to register point clouds for an autonomous driving vehicles (ADV), the operations including:
• determining a frame pair size of one or more pairs of related poses of a plurality of poses of a plurality of point clouds of a region to be mapped,
• separating the region containing the plurality of point clouds and the corresponding poses into a plurality of clusters in response to the frame pair size being greater than a threshold,
• applying separately a first optimization model to the plurality of point clouds corresponding to the plurality of clusters to register the point clouds,
• merging the registered point clouds from the plurality of clusters to perceive a driving environment of the region surrounding the ADV, and
• repeatedly separating the registered point clouds from the plurality of clusters into an additional plurality of clusters and applying the first optimization model to the plurality of point clouds corresponding to the additional plurality of clusters until the registered point clouds converge.