Robot scene self-adaptive pose estimation method based on RGB-D camera

Abstract

The invention discloses a robot scene self-adaptive pose estimation method based on RGB-D camera. scene two-dimensional color image information of adjacent frames and space depth information corresponding to pixels of the two-dimensional color image are acquired based on a RGB-D camera; when the two-dimensional color image feature points are sufficient, ORB operators are adopted to extract features, the matching strategy provided by the invention is adopted to carry out accurate matching, and three-dimensional pose estimation is solved based on the pose estimation algorithm of the matching feature points; when the feature points are insufficient, the improved ICP algorithm provided by the invention is adopted to solve the three-dimensional pose estimation; then, a complete switching criterion is designed to fuse the two pose estimation methods; finally, pose estimation obtained through the two methods is optimized through a light beam adjustment algorithm, and finally smooth and accurate three-dimensional pose estimation is obtained. The three-dimensional pose estimation algorithm has the outstanding advantages of high robustness, high precision, small calculation amount, adaptability to different scenes and the like.

Description

technical field [0001] The invention belongs to the field of robot control, in particular to a robot scene adaptive pose estimation method based on an RGB-D camera. Background technique [0002] Real-time robust and high-precision 3D pose estimation is one of the research difficulties and hotspots in the field of robotics. Its goal is to estimate the change in 3D space pose of the robot at two adjacent moments in real time. It is a robot SLAM (real-time positioning and map Creation), motion tracking, AR (Augmented Reality) and other core content. Traditional navigation systems based on inertial sensors are widely used in pose estimation, but there are problems such as drift and error accumulation, so the accuracy and reliability of the corresponding pose estimation are low. Compared with the inertial navigation system, the vision-based pose estimation does not have the problem of physical drift, and the cumulative error can be effectively eliminated through the global visua...

AI Technical Summary

Problems solved by technology

However, the defect of this type of method is that it relies heavily on the selection of feature points. First, the mismatched point pairs in the feature point set will have a serious impact on the initial value of the 3D estimation; secondly, the algorithm is only effective for scenes with many image feature points. If the scene If the feature points are sparse, it is easy to fail to track or the error is extremely large, which will directly lead to the failure of the algorithm, so that a stable pose estimation cannot be obtained.

In the actual robot operating environment, some regional scene features are rich, and some regional scene features are sparse, and the single-considered pose estimation method is difficult to work

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