Distributed SLAM (simultaneous localization and mapping) method on basis of global optimal data fusion

A globally optimal, data fusion technology, applied in navigation computing tools and other directions, can solve problems such as comprehensive analysis and evaluation of unfused systems, blindness in fusion system design, and ambiguity obstacles.

Inactive Publication Date: 2016-07-06
BEIJING UNIV OF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

People cannot make a comprehensive analysis and evaluation of the object-oriented fusion system, which makes the design of the fusion system somewhat blind
[0008] (2) The ambiguity of association is the main obstacle in data fusion
[0010] (3) The fault tolerance or robustness of the fusion system has not been well resolved

Method used

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  • Distributed SLAM (simultaneous localization and mapping) method on basis of global optimal data fusion
  • Distributed SLAM (simultaneous localization and mapping) method on basis of global optimal data fusion
  • Distributed SLAM (simultaneous localization and mapping) method on basis of global optimal data fusion

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Experimental program
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Embodiment Construction

[0076] Step 1: Build the model and linearize.

[0077] The entire state vector is divided into five-dimensional states of robot pose estimation and landmark estimation, and the state vector is: x v =[x L ,y L ,φ L ,x i ,y i ] T , X L ,y L ,φ L Is the state of the car, x i ,y i It is the state of the landmark. The Jacobian matrix H of the observation model after extended Kalman linearization is equation (5).

[0078] Step 2: Initialization.

[0079] Establish a coordinate system, take the position of the robot at the initial time as the origin of the coordinates, and use the directions of true east and true north as the positive directions of the x-axis and y-axis; the global map is initialized, and the robot is at the initial position, using the location information of the landmark points measured by sensor Environment map, and store it as a global map together with the pose of the robot at the initial moment (ie the position and angle of the robot).

[0080] Step 3: Map matching and...

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Abstract

The invention discloses a distributed SLAM (simultaneous localization and mapping) method on the basis of global optimal data fusion.The distributed SLAM method includes that integral state vectors are divided by distributed structures into five dimensions of states including robot pose estimation and road sign estimation, robot pose describing observation distribution probability formulas which ought to be subjected to centralized computing in a matrix form are processed in a distributed manner, a plurality of parallel and independent sub-filters are individually established according to each effective road sign point, robot pose estimation results of the sub-filters are fused in master filters, fusion results of the sub-filters are subjected to feedback correction by the aid of a global predictor, and the global optimal robot pose estimation results can be ultimately obtained.The distributed SLAM method has the advantages that algorithms of the distributed SLAM method are compared to centralized algorithms via real experiments, and accordingly the feasibility and the effectiveness of the distributed SLAM method are proved.

Description

Technical field [0001] The distributed extended Kalman SLAM algorithm based on global optimal data fusion uses a distributed structure to divide the entire state vector into a five-dimensional state of robot pose estimation and road sign estimation, and describes the robot position that should be calculated in a matrix form. The posture observation distribution probability formula is distributed, and multiple parallel and independent sub-filters are established separately according to each effective road marking point, and then the robot’s pose estimation results of the sub-filters are fused in the main filter, and The fusion result of the sub-filter is corrected by the global predictor feedback, and finally the global optimal robot pose estimation result is obtained. It belongs to the field of autonomous robot navigation. Background technique [0002] SLAM (Simultaneous Localization and Mapping) is synchronous positioning and map construction. Its basic idea is to let the robot...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 裴福俊武小平程雨航严鸿
Owner BEIJING UNIV OF TECH
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