Filtering combined laser SLAM mapping method and device

Abstract

The invention discloses a RBPF-based filtering combined laser SLAM mapping method and device combining an extended information matrix, and the method is characterized by comprising the following stepsof estimating the current pose of the robot based on the current sensor observation information of the robot; performing map matching processing based on the current sensor observation information ofthe robot and the current pose of the robot to obtain map information; performing proposed distribution calculation based on the pose of the robot in the last moment, the current sensor observation information and the map information to obtain a proposed distribution probability value; performing extended information filtering update on the sampled particles based on the proposed distribution probability value; performing re-sampling processing on the particles in the updated particle set according to the weight value to obtain a re-sampled particle set; and updating the map information basedon the re-sampled particle set. In the embodiment of the invention, the real-time operation efficiency of the robot using laser sensors to map and locate in the indoor non-linear structure environment can be effectively improved.

Description

technical field [0001] The present invention relates to the technical field of intelligent robots, and in particular to a method and device for constructing a filter-combined laser SLAM map based on RBPF combined with an extended information matrix. Background technique [0002] Simultaneous Localization and Mapping (SLAM, Simultaneous Localization and Mapping) technology is a major research hotspot in the field of robot autonomous navigation technology, and it is also a key technology in the practical application of robots. Lidar is an active detection sensor that does not depend on external lighting conditions and has high-precision ranging information; the Lidar SLAM method is still the most widely used method in the robot SLAM method; but in indoor complex nonlinear environments, The existing lidar SLAM method has the problems of low computing efficiency and low detection accuracy. [0003] Patent CN108387236A discloses a laser SLAM method based on Extended Kalman Filte...

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Problems solved by technology

[0004] This patented technology is based on the extended Kalman filter (EKF) laser SLAM method. Although the extended Kalman can be used to solve the uncertainty of the measurement noise of the nonlinear system, this method needs to maintain a high-latitude covariance matrix to describe The uncertainty of SLAM has problems such as large amount of calculation, complex algorithm, and low operational efficiency, which limits the actual application range; the laser SLAM method based on Extended Kalman Filter (EKF) requires the input noise to satisfy the Gaussian distribution, and is simple Therefore, it is difficult for mobile robots to achieve efficient and accurate positioning and map construction in indoor complex nonlinear environments.

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