KR-20260067574-A - METHOD, COMPUTING DEVICE AND COMPUTER PROGRAM FOR HIGH-PRECISION POSITION ESTIMATION OF ROBOT USING 2D SLAM ALGORITHM

KR20260067574AKR 20260067574 AKR20260067574 AKR 20260067574AKR-20260067574-A

Abstract

A method, apparatus, and computer program for high-precision position estimation of a robot using a 2D SLAM algorithm are provided. The method for high-precision position estimation of a robot using a 2D SLAM algorithm is a method performed by a computing device, comprising the steps of: estimating the position of a target robot using a low-resolution map layer having low resolution for a predetermined area; and correcting the estimated position using a high-resolution map layer having high resolution when the estimated position enters a pre-specified area.

Inventors

정진용

Assignees

리보틱스(주)

Dates

Publication Date: 20260513
Application Date: 20241106

Claims (6)

In a method performed by a computing device, A step of estimating the position of a target robot using a low-resolution map layer having low resolution for a predetermined area; and If the above-mentioned estimated location enters a pre-specified area, the method includes the step of correcting the above-mentioned estimated location using a high-resolution map layer having high resolution. High-precision position estimation method for a robot using a 2D SLAM algorithm.
In paragraph 1, The above low-resolution map layer is, It is used for location initialization, consists of a single map containing a map of all areas, and The above high-resolution map layer is, Characterized by generating a high-resolution map patch for a specified location according to user settings, constructing a KD-tree with the center point of the high-resolution map patch, and using it to correct the position when the target robot enters the patch. High-precision position estimation method for a robot using a 2D SLAM algorithm.
In paragraph 1, The step of estimating the position of the above-mentioned target robot is, A method comprising the step of estimating the position of the target robot in the low-resolution map layer based on a particle filter. High-precision position estimation method for a robot using a 2D SLAM algorithm.
In paragraph 1, The step of correcting the above-mentioned estimated position is, A step of checking whether there is a high-resolution map patch near the target robot using a KD-tree based on the location estimated using the low-resolution map layer; When the target robot enters the high-resolution map patch, a step of generating a high-resolution cost map using the high-resolution map; and A method comprising the step of correcting the position estimated using the low-resolution map layer by performing optimization based on the generated high-resolution cost map using the position estimated using the low-resolution map layer as an initial value. High-precision position estimation method for a robot using a 2D SLAM algorithm.
processor; Network interface; Memory; and It includes a computer program that is loaded into the memory and executed by the processor, An instruction for estimating the position of a target robot using a low-resolution map layer having low resolution for a predetermined area; and Including an instruction to correct the estimated location using a high-resolution map layer having high resolution when the estimated location enters a pre-specified area, High-precision position estimation method for a robot using a 2D SLAM algorithm.
Combined with a computing device, A step of estimating the position of a target robot using a low-resolution map layer having low resolution for a predetermined area; and A computer program stored on a recording medium readable by a computing device for executing a high-precision position estimation method of a robot using a 2D SLAM algorithm, comprising the step of correcting the estimated position using a high-resolution map layer having high resolution when the estimated position enters a pre-specified area.

Description

Method, Computing Device and Computer Program for High-Precision Position Estimation of Robot Using 2D Slam Algorithm Various embodiments of the present disclosure relate to a method, apparatus, and computer program for high-precision position estimation of a robot using a 2D SLAM algorithm, and more specifically, to a method, apparatus, and computer program for high-precision position estimation of a robot using a 2D SLAM algorithm that can estimate the accurate position of a robot while reducing errors occurring in the existing Particle Filter method and increasing computational efficiency by using a 2D SLAM algorithm for high-precision position estimation. Existing 2D localization algorithms have generally used a particle filter-based position estimation method. A particle filter is a method that assumes multiple particles (points) represent the robot's position, matches sensor measurements based on the position of each particle to calculate accuracy as a cost, and estimates the position based on this. This method has the advantage of providing a possible prediction of the robot's actual position by generating multiple virtual positions to perform position estimation. However, the Particle Filter method has several limitations. First, while the Particle Filter can test various error scenarios as the number of particles increases, the computational load increases linearly with the number of particles. This requires significant computational resources, which can lead to performance degradation in situations requiring accurate real-time location estimation. Additionally, because the Particle Filter determines costs discretely, there is a problem where errors may occur if a particle is not present at the precise ground truth location. For this reason, 2D SLAM (Simultaneous Localization and Mapping) algorithms for high-precision localization have recently been attracting attention. 2D SLAM algorithms provide the ability for a robot to create a map of its surrounding environment while moving and simultaneously determine its own location. This has the advantage of allowing the robot to autonomously search a given space and continuously update its location by comparing it with a previously constructed map. The aforementioned background technology is one that the inventor possessed or acquired in the process of deriving the content of the present disclosure, and it cannot be considered as prior art disclosed to the general public prior to the filing of this application. The following drawings attached to this specification illustrate preferred embodiments of the present disclosure and serve to further enhance understanding of the technical concept of the present disclosure together with the detailed description of the invention; therefore, the present disclosure should not be interpreted as being limited only to the matters described in such drawings. FIGS. 1 to 3 are drawings illustrating a conventional Grid Map method for estimating location. FIG. 4 is a diagram illustrating, exemplarily, the structure of a map generated according to various embodiments of the present disclosure. FIG. 5 is a diagram illustrating a low-resolution map layer on which a pose graph of a robot is displayed in various embodiments. FIG. 6 is a diagram illustrating a high-resolution area selected by a user through a low-resolution map layer in various embodiments. FIG. 7 is a drawing illustrating a completed high-resolution map patch in various embodiments. FIG. 8 is a diagram illustrating, in various embodiments, an exemplary kd-tree of a submap for fast searching. Figure 9 is a diagram illustrating a conventional 2D Localization algorithm. Figure 10 is a diagram showing a cost map generated by transforming a 2D occupancy map. FIG. 11 is a drawing illustrating the hardware configuration of a computing device according to various embodiments of the present disclosure. The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the present disclosure, and the present disclosure is defined only by the scope of the claims. The terms used herein are for describing the embodiments and are not intended to limit the disclosure. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, "comprises" and/or "comprising" do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout this specification, the same reference numerals refer to the same components, and "and/or" includes each of the mentioned c