Object pose estimation based on stereo vision with improvedK‐DtreeICPalgorithm

Summary With the wide application of stereovision in SLAM, object pose estimation has gradually become one of the research hotspots. This article proposes an object pose estimation for robotic grasping based on stereo vision with improved K‐D tree ICP algorithm. The feature points and feature descriptors of the point cloud of the object to be captured are extracted, and the feature template set is established. The SAC‐IA algorithm is used to carry out initial registration of the point cloud of the target, and the ICP algorithm based on K‐D tree is used for fine registration. The experimental results show that the average coincidence degree of the final registration of the proposed object pose estimation method reaches 94.1%, and the accurate 6D pose of the object to be grasped is obtained.