Rescue robot three-dimensional environment map real-time construction method

Abstract

The invention discloses a rescue robot three-dimensional environment map real-time construction method. According to the method, an efficient speeded up robust feature (SURF) algorithm is adopted to carry out recognition and matching of feature points; next, an interval analysis method is proposed, roughly matched pose transformation matrixes are generated. The method has good robustness and can contain all possible values under the situation that one set of constraint conditions are given. In order to overcome the defect that the iterative process probably can not converge the globally optimal solution through an ICP algorithm, an improved closest point select method is proposed: for a measure point in a point cloud A, three points, closest to the measure point, in a point cloud B are solved to form a plane, an intersection of normal vector passing the measurement point in the plane and the plane is regarded as the closest point, and consequently the accuracy of the algorithm is well improved. In addition, the approach combining an inertial management unit (IMU), odometer data and the pose transformation matrixes is adopted, so that the algorithm has better robustness, and accurate location can be achieved under the condition that feature point matching is unsuccessful or the environment dynamically changes.

Description

technical field [0001] The invention relates to a three-dimensional reconstruction method based on the field of rescue robots, provides a solution for three-dimensional map construction, and provides a visual basis for rescue in a disaster environment. Background technique [0002] In the field of robotics research, the exploration and establishment of unknown environments has always been a research hotspot. The construction of the 3D environment map provides an intuitive basis for the robots exploration and search and rescue work in unknown environments. [0003] Document "RGB-D mapping: Using depth camera for dense3D modeling of indoor environments, 12 th ISER.2010, 20: p22-25" discloses a 3D reconstruction method based on RGB-D sensor, which is also the most commonly used algorithm at present. This method uses SIFT algorithm for feature point extraction and matching, and then RANSAC The algorithm eliminates the wrongly matched point pairs to obtain the rough matching a...

AI Technical Summary

Problems solved by technology

The SIFT algorithm used in this algorithm is not efficient and has a large time complexity; the ICP algorithm requires an inclusion relationship between the two paired point clouds, otherwise it will affect the convergence result of the ICP algorithm. The initial relative positions are closely related, and the two sets of point clouds are required to have relatively similar initial positions; this algorithm is suitable for use in a static environment, and the matching effect of feature points in a dynamically changing environment is poor

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