Simultaneous localization and mapping method based on distributed edge unscented particle filter

A particle filtering and synchronous positioning technology, used in mapping and navigation, measuring devices, instruments, etc.

Active Publication Date: 2014-03-19
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0006] Aiming at the shortcomings of the existing distributed particle filter SLAM method, the present invention proposes a dis

Method used

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  • Simultaneous localization and mapping method based on distributed edge unscented particle filter
  • Simultaneous localization and mapping method based on distributed edge unscented particle filter
  • Simultaneous localization and mapping method based on distributed edge unscented particle filter

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Embodiment Construction

[0063] 1. System model

[0064] Establish the coordinate system, the robot motion model and the state model of the landmark point are established in the robot coordinate system, the observation model of the landmark point is established in the sensor coordinate system, and the vehicle robot coordinate system is as follows: image 3 shown. From the perspective of probability, in the SLAM problem, the motion state of the mobile robot at time k can be determined by the motion state of the robot at time k-1. Assuming that the landmarks in the environment are stationary, the following robot motion model can be established:

[0065]

[0066] in, and are the position changes of the robot in the x-direction and y-direction and the robot angle changes between the two moments before and after, respectively, and α is the change of the robot angle between the two moments before and after, is the robot angle value at the present moment calculated according to the angle change amo...

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Abstract

The invention relates to a simultaneous localization and mapping method based on distributed edge unscented particle filter. First, a coordinate system is built and an environmental map is initialized; then subfilters are built for each landmark point with successful matching respectively; next, based on a robot motion model, a particle swarm is generated in each subfilter respectively, and the state vector and the variance of each particle are obtained; noise is introduced, particle state vectors after extension are calculated by utilization of unscented transformation, the particles after extension are updated and the particle swarms are optimized; then particle weights are calculated and normalization is carried out, and aggregated data of each subfilter are subjected to statistics and the data are sent to a master filter; next, global estimation and variance are calculated; then the effective sampling draw scale and sampling threshold of each subfiter are determined, the subfilters with severe particle degeneracy are subjected to resampling; then the state vectors and the variances of the robot are output, and stored in a map. Finally, landmark point states are updated by utilization of kalman filtering algorithm until the robot is no longer running.

Description

1. Technical field [0001] The method of synchronous positioning and map construction based on distributed edgeless particle filter can enable the robot to realize autonomous positioning and map creation in an unknown environment, which belongs to the field of robot autonomous navigation. 2. Background technology [0002] The basic idea of ​​Simultaneous Localization and Mapping (SLAM) is to let the robot move from an unknown position in an unknown environment, estimate its own position through the information of the landmark points scanned by its own sensor, and build an augmented reality map at the same time. Quantitative map. Since the birth of mobile robots, the research on positioning problems has been closely related to the problem of map creation. Therefore, synchronous positioning and map construction have important theoretical and application values. Many scholars believe that they are the key to realizing the real autonomous movement of robots. Used in many fields....

Claims

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Application Information

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IPC IPC(8): G01C21/00
CPCG01C21/00G01C21/20
Inventor 裴福俊李昊洋武玫
Owner BEIJING UNIV OF TECH
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