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Method for simultaneous localization and mapping of mobile robot based on improved particle filter

A mobile robot and map construction technology, applied in navigation computing tools and other directions, can solve the problems of particle filter with large calculation amount and particle degradation, etc., achieve good effect, convenient implementation, and reduce calculation amount

Inactive Publication Date: 2017-10-13
HARBIN ENG UNIV
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Problems solved by technology

However, the particle filter has a large amount of calculation, and after many iterations, the particles will degenerate.

Method used

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  • Method for simultaneous localization and mapping of mobile robot based on improved particle filter
  • Method for simultaneous localization and mapping of mobile robot based on improved particle filter
  • Method for simultaneous localization and mapping of mobile robot based on improved particle filter

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Embodiment Construction

[0042]The mobile robot localization and map construction method based on the improved particle filter, by using the improved particle filter algorithm, when selecting the importance probability density function, the proposed distribution combining the motion model and the observation value is selected. In the resampling stage of the particle filter, the threshold for judging the number of effective particles is set as a dynamic threshold, and different thresholds are selected according to the complexity of the environment; the genetic algorithm is introduced, and the selection, crossover, and mutation in the algorithm are selected. Genetics is also used in the selection of particles, which not only ensures the sufficient number of particles, but also prevents the singleness of particles.

[0043] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0044] First, initialize the pose of the robot at the initial moment, and gene...

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Abstract

The invention discloses a method for simultaneous localization and mapping of a mobile robot based on an improved particle filter. The method comprises the following steps: initializing an initial-moment pose of a robot; obtaining a t-moment prior probability density function according to the pose information at a t-1 moment, and generating a sampling particle set p; initializing the weights of particles; selecting an importance probability density function, generating a new sampling particle set q, calculating the weights of particles, updating the weights of the particles, and normalizing the weights; calculating the weighted sum of random sample particles at current moment t to express posterior probability density, and obtaining the moving pose and environmental map information; judging whether a new observed value is input; if so, returning; otherwise, ending the cycle; before returning, judging whether resampling is needed or not. According to the difference of the system state, a dynamic threshold is set for judgment, and a genetic algorithm is combined. According to the method disclosed by the invention, influence of a problem of particle degeneration on SLAM is reduced, and the calculated amount of the SLAM problem is reduced.

Description

technical field [0001] The invention relates to the technical field of autonomous positioning and environment perception of a mobile robot, in particular to a method for simultaneous positioning and map construction of a mobile robot based on an improved particle filter. Background technique [0002] With the vigorous development of robot technology, the autonomous cognition ability of robots to the unknown environment has become a research hotspot in robotics. Constructing a map of the unknown environment, that is, the positioning and navigation of mobile robots, is one of the key research contents and research hotspots of robot autonomous cognition. Among them, Simultaneous Localization and Mapping (SLAM) is an effective means for mobile robots to achieve localization and navigation, that is, the robot extracts features based on the acquired sensor-related data, and after feature matching, finally independently constructs a map of the unknown environment in which the robot...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 朱齐丹王靖淇纪勋张欣
Owner HARBIN ENG UNIV
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