Map generation and navigation obstacle avoidance method based on automatic driving platform

A map generation and automatic driving technology, applied in surveying and mapping, navigation, navigation, navigation computing tools, etc., can solve the problems of lack of map information, map dislocation, high system resource occupancy, etc., to improve the effect, avoid map dislocation, and improve operating efficiency high effect

Pending Publication Date: 2020-08-25
TIANJIN UNIV
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Problems solved by technology

[0012] The invention provides a method for map generation and navigation obstacle avoidance based on an automatic driving platform. The invention can avoid problems such as map dislocation, lack of map information, and high system resource occupancy in existing map generation algorithms, thereby improving the effect of map generation. The navigation and obstacle avoidance method proposed by the present invention can be integrated with existing different navigation and obstacle avoidance algorithms, and has validity, completeness and modifiability, as described below for details:

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  • Map generation and navigation obstacle avoidance method based on automatic driving platform
  • Map generation and navigation obstacle avoidance method based on automatic driving platform
  • Map generation and navigation obstacle avoidance method based on automatic driving platform

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Embodiment 2

[0048] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas and examples, see the following description for details:

[0049] The map generation algorithm is an improved Hector SLAM algorithm based on EKF, and the navigation obstacle avoidance algorithm is a navigation obstacle avoidance algorithm that combines A* and DWA, including:

[0050] Step 201: use EKF to fuse the data of wheel odometry (odometry), inertial measurement unit (Inertial Measurement Unit, IMU), and laser radar (Lidar) to obtain new fused odometry data;

[0051] In the specific implementation, odometry is used to initialize the system state quantity and covariance matrix. The covariance matrix is ​​initialized to a non-zero matrix, and the system input is initialized to [0,0] T . Monitor the information of each sensor and odometry through the rostopic command of the robot operating system (ROS). system input [u 1 , u 2 ] T It is the displacement and ...

Embodiment 3

[0092] The map generation algorithm proposed by the present invention is an improved Hector SLAM algorithm based on EKF. In the experiment of the map generation algorithm for automatic driving, the evaluation of the algorithm is carried out by analyzing the effect of the generated map.

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Abstract

The invention discloses a map generation and navigation obstacle avoidance method based on an automatic driving platform. The method comprises the following steps of obtaining a space map of car driving based on an EKF improved Hector SLAM map algorithm; utilizing the obtained space map, and utilizing a navigation obstacle avoidance algorithm integrating A* and DWA to obtain a route plan of a trolley in the driving process in the space map; and evaluating and verifying the effects on a map generation algorithm and a navigation obstacle avoidance algorithm through experiments. The navigation obstacle avoidance method provided by the invention can be fused with existing different navigation obstacle avoidance algorithms, and has effectiveness, completeness and correctability.

Description

technical field [0001] The invention relates to the field of automatic driving in artificial intelligence, in particular to a method for map generation and navigation obstacle avoidance based on an automatic driving platform. Background technique [0002] Extended Kalman Filter (Extended Kalman Filter, EKF) is a nonlinear version of Kalman filter. It is mainly an algorithm that processes the input and output observation data of the system using a linear state equation, and then performs an optimal estimation of the system state according to the result. Kalman filtering is a linear equation, which was originally used for radar tracking experiments, and later developed into a filtering algorithm that can be used in various fields such as navigation and communication. [0003] Map generation belongs to the category of Simultaneous Localization And Mapping (SLAM). The problem can be described as putting the robot into an unknown environment, and then let it move for position e...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/20
CPCG01C21/005G01C21/20
Inventor 王建荣王子璇于瑞国于健徐天一赵满坤朱思翰
Owner TIANJIN UNIV
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