Mobile robot indoor positioning mapping method based on multi-sensor fusion

Abstract

The invention discloses a mobile robot indoor positioning mapping method based on multi-sensor fusion, and the method comprises the steps: carrying out the data fusion of a laser radar, an inertial measurement unit (IMU) and a wheel type odometer, building a robot motion model based on the fusion of the odometer and the IMU, optimizing a suggested distribution function through employing a laser radar observation information fusion motion model, and achieving the positioning mapping of a robot. The problems of large system prediction distribution error and particle memory explosion are solved;a novel particle resampling strategy method is given, the particle diversity is kept, the particle dissipation problem is relieved, and the mapping efficiency and the mapping precision are obviously improved.

Description

technical field [0001] The invention relates to the field of intelligent technology, in particular to an indoor positioning and mapping method for a mobile robot based on multi-sensor fusion. Background technique [0002] SLAM (simultaneous localization and mapping, SLAM) is the basis for mobile robots to achieve precise autonomous navigation, and positioning and mapping are key technologies for robot autonomous navigation. The principle of particle filter is a non-parametric filter based on Bayesian reasoning and importance sampling, which can deal with nonlinear and multimodal distributions. Thrun et al. first proposed the SLAM method based on particle filter, assigning weights to the particles in the space state, and obtaining the posterior probability distribution of the state of the robot system, making the positioning more accurate, but with the increase of the number of particles, the complexity of map construction also increased. Murphy et al proposed to use the Ra...

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Problems solved by technology

[0003] However, the SLAM method based on a single sensor in the prior art is unstable when the indoor environmental conditions are poor, the robot’s movement speed or steering is too fast, for example: the laser radar sensor has a limited scanning observation distance and is easily affected by complex geometric structures in the environment.

The camera has certain requirements on the lighting conditions of the surrounding environment of the robot. The encoder motor will produce cumulative errors after a long time of work. In the traditional RBPF-SLAM algorithm, there are problems such as large particle suggestion distribution error, particle consumption, and long algorithm running time. As a result, the existing indoor positioning and mapping methods for mobile robots have large prediction distribution errors and particle memory explosion problems

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