Laser radar SLAM algorithm and inertial navigation fusion positioning method

A laser radar, fusion positioning technology, applied in surveying and navigation, navigation, road network navigator and other directions, to achieve the effect of wide use prospects

Pending Publication Date: 2021-06-08
BIT INTELLIGENT VEHICLE TECH CO LTD +2
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Problems solved by technology

[0003] Although there are currently many researches on lidar-based vehicle positioning, there are still major challenges for the complex environment faced by unmanned driving:

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  • Laser radar SLAM algorithm and inertial navigation fusion positioning method
  • Laser radar SLAM algorithm and inertial navigation fusion positioning method
  • Laser radar SLAM algorithm and inertial navigation fusion positioning method

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Embodiment Construction

[0021] Preferred embodiments of the present invention will be specifically described below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the application and are used together with the embodiments of the present invention to explain the principle of the present invention.

[0022] A specific embodiment of the present invention discloses a lidar mileage calculation method based on a characteristic probability grid map, such as figure 1 shown, including the following steps:

[0023] Data preprocessing, feature probability map update, point cloud and map matching and update pose.

[0024] Data preprocessing is the preliminary processing, downsampling and classification of the lidar point cloud;

[0025] The main function of the feature probability map update part is to manage the map, extract point cloud distribution features, update the grid, etc.;

[0026] Point cloud matching and pose update is through the data associati...

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Abstract

The invention firstly provides a laser radar SLAM algorithm-CPFG (Closet Probability and Feature Grid) algorithm based on a feature probability grid map. The algorithm utilizes three-dimensional laser radar data to create and update a grid map of line, surface and Gaussian distribution characteristics and occupancy probability in real time, and combines a robust mahalanobis distance as an optimization function to carry out real-time pose estimation. The algorithm mainly comprises three steps of point cloud preprocessing, matching of the point cloud and the characteristic probability grid map and pose estimation, and updating of the feature probability grid map. Compared with several current mainstream algorithms, the laser radar SLAM algorithm has better performance in the aspects of real-time performance and positioning precision. Afterwards, attitude information of inertial navigation is fused, the high displacement precision of the laser radar SLAM and the low attitude drift characteristic of inertial navigation are combined, the relative positioning precision of the method can reach about one thousandth, and the method has wide application prospects in the field of unmanned driving positioning.

Description

technical field [0001] The present invention relates to the technical field of unmanned vehicles, in particular to a lidar SLAM algorithm based on a feature probability grid map-CPFG (Closet Probability and Feature Grid, nearest neighbor probability feature grid) algorithm, which is then fused with inertial navigation for positioning. Background technique [0002] The SLAM problem first originated in 1986 and has a history of more than 30 years. Durrant-Whyte and Bailey et al. summarized the development of SLAM. The research of SLAM in the first 20 years was mainly based on filter theory. In 1988, SmithR et al. proposed EKF-SLAM. First, the extended Kalman filter was applied to the SLAM problem. This algorithm needs to construct a feature map and estimate the position of each feature and the position of the vehicle at the same time. The computational complexity is high and robust Insufficient sex. Montemerlo et al. proposed FastSLAM, which first used the particle filter me...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/32G01C21/16
CPCG01C21/165G01C21/32
Inventor 齐建永刘宇航张哲华龚建伟陈慧岩熊光明吴绍斌
Owner BIT INTELLIGENT VEHICLE TECH CO LTD
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