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G2o-based SLAM rear end optimization algorithm method

A technology for optimizing algorithms and nodes, used in computing, computer components, navigation computing tools, etc.

Inactive Publication Date: 2015-08-19
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a g2o-based SLAM back-end optimization algorithm method, which solves the back-end optimization problem of synchronous positioning and map construction, reduces the influence of wrong closed-loop constraints or eliminates wrong closed-loop constraints, and corrects the robot. The pose sequence enables the front-end to build a more accurate map

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Embodiment 1

[0092] In the embodiment of the present invention, the proposed DCS1 algorithm method is integrated based on the g2o platform. g2o is an open source framework for general graph optimization. This framework mainly solves the problem of nonlinear least squares based on graph optimization, which is known in the pose graph Back-end optimization under the circumstances. In the implementation process, the back-end optimization problem of SLAM problem was solved using the proposed DCS1 algorithm in the g2o platform, and the optimized pose sequence was output.

[0093] Generally, SLAM algorithms based on graph optimization include two steps: graph construction and graph optimization. The construction of the graph is called the front end, and the optimization of the graph is called the back end. figure 1 Shows the whole process of graph-based SLAM. The front end processes the raw sensor data obtained by the robot and performs data fusion to complete the construction of the pose map. Af...

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Abstract

The invention discloses a g2o-based SLAM rear end optimization algorithm method, pose node information of a robot is used as input data, a weight factor is added for each edge by focusing closed loop restriction with a rear end optimization algorithm, a value of the weight factor is obtained according to a mathematic relation between the derived weight factor and an information matrix, and after the read end optimization algorithm, the pose node information of the robot is corrected; a built-in optimization strategy of g2o is further optimized based on a g2o platform by a least square method, and a post node path more according with an actual path condition is constructed. The g2o-based SLAM rear end optimization algorithm method adopts a DCSI algorithm, and is used for solving the problem of robutness rear end optimization, complexity is reduced, operation time is reduced and the rate of convergence rate is improved. The g2o-based SLAM rear end optimization algorithm method has important meaning in correction and optimization of a topological map in an unknown environment.

Description

Technical field [0001] The invention relates to the field of synchronous positioning and construction of maps (SLAM) of mobile robots, in particular to a g2o-based SLAM back-end optimization algorithm method. Background technique [0002] Since it was proposed in 1980, the SLAM problem has become an important research direction in robotics, and it is the basis for robots to achieve true autonomy in unknown environments. SLAM is essentially a state estimation problem, which can be divided into filtering methods and smoothing methods according to estimation techniques. Common filtering methods include extended Kalman filter EKF (extended Kalman filters), sparse extended information filter EKFs, particle filter and so on. SLAM based on graph optimization was introduced as early as 1997. It uses pose graphs to model SLAM problems. In recent years, many algorithms based on graph optimization have been proposed, the famous ones are Olson, TreeMap, TORO, iSAM, and some open source fra...

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Application Information

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IPC IPC(8): G06F17/30G06K9/00G01C21/00G01C21/20
CPCG06F16/29G01C21/005G01C21/20G06V20/00
Inventor 张亮沈沛意朱光明宋娟刘静
Owner XIDIAN UNIV
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