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Method for dynamic correction during SLAM (simultaneous localization and map building) of mobile robot

A mobile robot and map creation technology, applied in the direction of navigation calculation tools, etc., can solve the problem of deviation between positioning and map influence, achieve the effect of improving accuracy and stability, and improving the path planning of mobile robots

Inactive Publication Date: 2018-10-12
JIANGSU MARITIME INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a simultaneous positioning and map creation correction method of a mobile robot to solve the problem of deviation caused by the error factors of invalid data caused by sensors and external obstacles on positioning and map

Method used

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  • Method for dynamic correction during SLAM (simultaneous localization and map building) of mobile robot
  • Method for dynamic correction during SLAM (simultaneous localization and map building) of mobile robot
  • Method for dynamic correction during SLAM (simultaneous localization and map building) of mobile robot

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Embodiment 1

[0059] Such as Figure 1-3 As shown, the present embodiment provides a dynamic correction method for simultaneous localization and map creation of a mobile robot, comprising the following steps:

[0060] Step1: Data collection and verification,

[0061] Analyze and sort out the deviation factors caused by the real-time positioning and map construction of historical message records;

[0062] Step2: Adjust revision parameters,

[0063] First, initialize the mobile robot, the default starting position is zero,

[0064] Then, according to the pose information of the mobile robot at time t-1, that is, the posterior probability density function of t-1, the state of the current moment is predicted through the existing prior knowledge, and the prior density function at time t is obtained. Probability density function is used to generate and collect particle sets, N particles are collected, and the weights of particles are initialized to 1 / N;

[0065] Then set the fitness function, t...

Embodiment 2

[0112] This embodiment provides a deduction process of a dynamic correction method for simultaneous positioning and map creation of a mobile robot. Each particle records the current position, speed and the optimal position that has been visited, which represents a solution of the algorithm. During the iterative update process, each particle moves towards the global optimum and the direction where it has reached the optimal position:

[0113]

[0114] x i (k)=X i (k)+V i (k+1)

[0115] x i (k) represents the position of the i-th particle in the k-th iteration, and the particle is based on V i (k+1) velocity from position X i (k) fly to X i (k+1) position, V i The speed of (k+1) consists of three parts: inertia, cognition and society. The inertia part simulates the last trajectory of the bird. ω is the inertia weight, which represents the influence of the previous trajectory on the current new trajectory. c 1 and c 2 is the acceleration coefficient of cognitive part ...

Embodiment 3

[0154] This embodiment provides a dynamic correction method for simultaneous positioning and map creation of a mobile robot, including the following steps:

[0155] Build motion and observation models for Qt and Rt tuning particles;

[0156] The proposed prior knowledge correction FastSLAM algorithm adds Q before the particle update t and R t In the process of adjustment, the method of updating and resampling of each particle still adopts the algorithm of FastSLAM2.0; due to Q t and R t The adjustment process is similar to the update process of FastSLAM2.0 particles, and the adjustment process is used to adjust Q t and R t The pose and environment information of the mobile robot (based on different coordinate systems and origin positions) as a special particle X [ex] Look, Q t and R t The adjustment process is as follows:

[0157] 1) Initialization: If the number of landmarks observed this time or last time is too small, it is impossible to determine the corresponding ...

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Abstract

The invention discloses a method for correction during SLAM (simultaneous localization and map building) of a mobile robot. The method comprises steps as follows: Step1: historical data are acquired and checked, and deviation factors of historical message records during SLAM are analyzed and arranged; Step 2: parameters are adjusted and modified, a fitness function is set, a search updating modelis built, and secondary sampling is performed; knowledge correction is verified, a fitness function of Q and R is defined according to the inconformity of close observation, and discreet values of Q and R are searched with an improved fractional order particle swarm algorithm, and correction of Qt and Rt is completed according to the discreet values; Step3: a new route is corrected, and updating of a particle cluster is completed through prediction, data association, posture updating, weight calculation and road sign updating; resampling is performed, the number of effective particles is calculated, and normalization resampling is performed. The problem of route deviation caused by ineffective data deviation factors because of unstable factors of a sensor and the outside is solved.

Description

technical field [0001] The invention relates to the technical field of mobile robot positioning, in particular to a dynamic correction method for simultaneous positioning and map creation of a mobile robot. Background technique [0002] Mobile robots can navigate autonomously through localization and mapping domain (SLAM) technology. [0003] In actual situations, the road surface, temperature, tire condition, tightness of the track, and driving speed affect the error of the inertial navigation system and the detection and transmission of sensitive sonar sensors, and invalid data information will be entered. There will also be unpredictable obstacle interference when exploring an unknown environment, such as being unable to move when encountering obstacles, slipping wheels, encountering "kidnapping", and road signs being affected by external forces. When positioning and creating maps for mobile robots in unknown environments, errors Poor accuracy under the influence of inva...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 吕太之陈勇张军王婷覃章健
Owner JIANGSU MARITIME INST
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