METHOD AND COMPUTING DEVICE FOR GLOBAL LOCALIZATION OF MOBILE ROBOTS

20240419182

18/702481

September 02, 2022

A method for global localization of a mobile robot is disclosed. The method compares a histogram value, obtained by quantifying geometric and structural features of each query submap image divided from a global map image, with geometric and structural features of submap images stored in a database to select a submap image which is the most similar to a query submap image, and performs the global localization of the mobile robot, based on coordinate information included in the selected submap image.

ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE (Daejeon, KR)

1. A method for global localization of a mobile robot, performed by a processor of a computing device equipped in the mobile robot, the method comprising: dividing a global map image into a plurality of query submap images; calculating a histogram value representing a geometric feature of each query submap image; calculating a reflection symmetry score representing structural feature information about each query submap image; calculating a submap similarity score between each query submap image and submap images stored in a database, based on the histogram value and the reflection symmetry score; and determining a submap image which is the most similar to the query submap image, based on the submap similarity score, and performing the global localization of the mobile robot, based on coordinate information included in the determined submap image.

2. The method of claim 1, wherein the dividing of the global map image into the plurality of query submap images comprises: generating the global map image by using a sensor equipped in the mobile robot; extracting a junction point, defining a point from which a movement path of the mobile robot branches, from the global map image; and dividing the global map image into the plurality of query submap images having a certain radius with respect to the junction point.

3. The method of claim 2, wherein the extracting of the junction point comprises: transforming the global map image into an edge map image, based on an image processing technique; and when an n×n image patch including a center pixel of the edge map image and peripheral pixels surrounding the center pixel is assumed, extracting the center pixel as the junction point when a pixel value of the center pixel differs from pixel values of at least three peripheral pixels.

4. The method of claim 1, wherein the calculating of the histogram value representing the geometric feature of each query submap image comprises: calculating a boundary histogram value representing a geometric feature of a boundary region, where the mobile robot is incapable of moving, in each query submap image; and calculating a free space histogram value representing a geometric feature of a free space region, where the mobile robot is capable of moving, in each query submap image.

5. The method of claim 4, wherein the calculating of the boundary histogram value comprises: transforming each query submap image into an edge map image, based on an image processing technique; sampling boundary points configuring the boundary region in the edge map image; pairing the sampled boundary points to generate a plurality of boundary point pairs; and calculating the boundary histogram value, based on a distance value between two boundary points included in each boundary point pair.

6. The method of claim 4, wherein the calculating of the free space histogram value comprises: transforming each query submap image into an edge map image, based on an image processing technique; extracting free space points configuring the free space region in the edge map image; pairing two free space points having a shortest path distance value among the extracted free space points to generate a plurality of free space point pairs; and calculating the free space histogram value, based on the shortest path distance value of each free space point pair.

7. The method of claim 1, wherein the calculating of the reflection symmetry score comprises: transforming each query submap image into an edge map image, based on an image processing technique; detecting axes of reflection symmetry in the edge map image, based on angles of line segments having a certain length; transforming the edge map image into a blurred image, based on an image blurred technique; and calculating, as the reflection symmetry score, a similarity score between the blurred image and a flipped blurred image flipped with respect to the detected axes of reflection symmetry.

8. The method of claim 7, further comprising rotating the edge map image so that the detected axes of reflection symmetry are vertically aligned, between the detecting of the axis of reflection symmetry and the transforming of the edge map image into the blurred image.

9. The method of claim 1, wherein the calculating of the submap similarity score comprises: calculating a first difference value between the histogram value of each query submap image and histogram values representing the geometric features of the submap images stored in the database; calculating a second difference value between the reflection symmetry score of each query submap image and reflection symmetry scores representing structural features of the submap images stored in the database; and calculating the submap similarity score, based on the first and second difference values.

10. The method of claim 9, wherein the calculating of the first difference value comprises: calculating a difference value between a boundary histogram value, representing the geometric feature of a boundary region where the mobile robot is incapable of moving in each query submap image, and boundary histogram values representing the geometric feature of a boundary region where the mobile robot is incapable of moving in the submap images; and calculating a difference value between a free space histogram value, representing the geometric feature of a free space region where the mobile robot is capable of moving in each query submap image, and free space histogram values representing the geometric feature of a free space region where the mobile robot is capable of moving.

11. A computing device for global localization of a mobile robot, the computing device comprising: an image divider configured to divide an occupancy grid map image into a plurality of query submap images; a feature extractor configured to calculate a histogram value representing a geometric feature of each query submap image and calculate a reflection symmetry score representing a symmetry feature of each query submap image; a submap similarity score calculator configured to calculate a submap similarity score between each query submap image and submap images stored in a database, based on the histogram value and the reflection symmetry score; and a global localization processor configured to determine a submap image which is the most similar to the query submap image, based on the submap similarity score, and perform the global localization of the mobile robot, based on coordinate information included in the determined submap image.

12. The computing device of claim 11, wherein the image divider divides the occupancy grid map image into a plurality of query submap images with respect to a junction point from which a movement path of the mobile robot branches, in the occupancy grid map image.

13. The computing device of claim 11, wherein the feature extractor comprises: a first geometric feature extractor configured to calculate a boundary histogram value obtained by quantifying distribution features of a boundary region where the mobile robot is incapable of moving, in each query submap image; a second geometric feature extractor configured to calculate a free space histogram value obtained by quantifying distribution features of a free space region where the mobile robot is capable of moving, in each query submap image; and a structural feature extractor configured to calculate a reflection symmetry score obtained by quantifying symmetricity of each query submap image.

14. The computing device of claim 11, wherein the submap similarity score calculator comprises: a first subtractor configured to calculate a difference value between a boundary histogram value obtained by quantifying geometric features of a boundary region in each query submap image and a boundary histogram value obtained by quantifying geometric features of a boundary region in a submap image stored in the database; a second subtractor configured to calculate a difference value between a free space histogram value obtained by quantifying geometric features of a free space region in each query submap image and a boundary histogram value obtained by quantifying geometric features of a free space region of the submap image stored in the database; and a third subtractor configured to calculate a difference value between the reflection symmetry score obtained by quantifying symmetricity of each query submap image and a reflection symmetry score obtained by quantifying symmetricity of the submap image stored in the database.

15. The computing device of claim 14, wherein the submap similarity score calculator performs an arithmetic operation on the difference values calculated by the first to third subtractors to calculate a similarity score between each query submap image and the submap images.

16. The computing device of claim 14, wherein the submap similarity score calculator further comprises an adder configured to summate the difference value calculated by the first subtractor and the difference value calculated by the second subtractor, and the submap similarity score calculator performs an arithmetic operation on the difference value calculated by the third subtractor as a weight and a value summated by the adder to calculate a similarity score between each query submap image and the submap images.

17. A method for global localization performed by a processor of a computing device equipped in a mobile robot, the method comprising: calculating a boundary histogram value obtained by quantifying distribution features of a boundary region where the mobile robot is incapable of moving, in each query submap image divided from a global map image; calculating a free space histogram value obtained by quantifying distribution features of a free space region where the mobile robot is capable of moving, in each query submap image; calculating a reflection symmetry score obtained by quantifying symmetricity of each query submap image with respect to an axis of symmetry extracted from each query submap image; comparing the boundary histogram value, the free space histogram value, and the reflection symmetry score of each query submap image with a boundary histogram value, a free space histogram value, and a reflection symmetry score of submap images input from a database to select a submap image, which is the most similar to each query submap image, from among submap images stored in the database; and performing the global localization of the mobile robot, based on coordinate information included in the selected submap image.

18. The method of claim 17, wherein the global map image is a 2D or 3D occupancy grid map image.

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