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Robot positioning method based on particle filtering

A robot positioning and particle filter technology, applied in the field of robotics, can solve the problems of single-line laser information not being so rich, not applicable, and the map does not save obstacle information, etc.

Active Publication Date: 2021-07-30
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

An existing SLAM system based on ORB feature points uses a camera to collect images to construct a map of visual feature points, thereby realizing relocation and continuous pose estimation. However, the map generated by this algorithm does not save obstacle information and cannot be applied to robot paths. planning and obstacle avoidance; while the graph-optimized laser SLAM system constructs a laser grid map that is convenient for robot path planning, and uses the data association of sensors such as lasers to achieve positioning. However, the single-line laser information is not so rich, resulting in repeated The positioning effect is not good; a ROS-based robot navigation framework including mapping, positioning, and path planning is one of the few open-source work that has opened up multiple modules, and many practical designs have been proposed for the robot to land

Method used

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  • Robot positioning method based on particle filtering
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  • Robot positioning method based on particle filtering

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Experimental program
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specific Embodiment approach

[0069] Experimental results and analysis

[0070] Experiment preparation: The platform used in this experiment consists of a self-made chassis with a differential wheel, a SCIK tim-561 single-line laser radar, a Xiaomi smart S1040 binocular camera, an ALUBI IMLPMS-ME1 model IMU and a The CPU is composed of i7-9750H Dell notebooks. The experimental site is a back-shaped indoor office site. In this site, three experiments on initial positioning, process positioning, and point positioning are carried out. In the initialization experiment, the robot's range of motion in the field was used to compare and test the average success rate and average positioning time of the initialization positioning of the two methods. The process positioning experiment is to let the robot move and compare the positioning quality of the two methods. The point-to-point positioning experiment is to measure the overall improvement effect from the system's final point-to-point positioning accuracy.

[00...

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Abstract

The invention discloses a robot positioning method based on particle filtering. The method comprises the following steps: (1) obtaining visual map coordinates; (2) converting map coordinates; (3) estimating the motion of a wheel speedometer and a gyroscope; and (4) carrying out Kalman filtering. The invention belongs to the technical field of robots, and particularly relates to a robot positioning method based on particle filtering, which combines vision and a gyroscope, utilizes a data association and multi-sensor fusion technology, improves the positioning effect and provides a more ideal technical scheme for scenes such as robot distribution and transportation.

Description

technical field [0001] The invention belongs to the technical field of robots, and specifically refers to a particle filter-based robot positioning method. Background technique [0002] Compared with traditional magnetic navigation, two-dimensional code navigation, reflector navigation and other methods, the autonomous mobile robot navigation system based on SLAM navigation has the advantages of not needing to modify the navigation environment, and can plan paths independently. It has been widely used in various indoor mobile in the robot. At present, a lot of work has been carried out from the five basic modules of map construction, positioning, perception, path planning and motion control to empower mobile robots to be autonomous and intelligent. [0003] As we all know, positioning is the core module of robot navigation. Other modules need to ensure the stable operation of the robot based on accurate positioning conditions. SLAM navigation uses external sensor informatio...

Claims

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Application Information

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IPC IPC(8): G01S17/93G01S17/86G01S17/89G01C22/00G01C21/16G06T7/277G06T7/73G06T17/05G06K9/62
CPCG01S17/93G01S17/86G01S17/89G01C21/165G01C22/00G06T7/277G06T7/73G06T17/05G06F18/22G06F18/25
Inventor 叶泳骏陈新度吴磊
Owner GUANGDONG UNIV OF TECH
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