Indoor robot navigation method based on environment characteristic detection

An indoor robot and environmental feature technology, applied in the field of robot navigation, can solve the problems of low intelligence and insufficient semantic information for positioning and navigation, and achieve the effect of convenient integration and high precision

Inactive Publication Date: 2019-05-07
BEIHANG UNIV +1
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Problems solved by technology

[0024] Aiming at the problem of insufficient semantic information and low intelligence of the current positioning and navigation, the present invention provides an indoor robot navigation method based on environmental feature detection. By establishing a semantic map, the positioning

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  • Indoor robot navigation method based on environment characteristic detection
  • Indoor robot navigation method based on environment characteristic detection
  • Indoor robot navigation method based on environment characteristic detection

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Embodiment Construction

[0051] The technical solutions of the present invention will be described below in conjunction with the drawings and embodiments.

[0052] The indoor robot navigation method based on environmental feature detection provided by the present invention generally includes the following steps:

[0053] Step 1: firstly establish an indoor object data set, the present invention uses SSD (Single Shot MultiBoxDetector, single object multi-box detector) to carry out object detection model training and testing, and then integrates with Gmapping algorithm to establish the semantics of grid map and object position combination map;

[0054] Step 2: Use the method of maximum likelihood estimation to perform global positioning of the robot. When the robot rotates in place, the object detection model is used to detect the object, and the semantic information in the semantic map is matched, and the maximum likelihood estimation method is used to estimate, and finally the global pose of the robo...

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Abstract

The invention provides an indoor robot navigation method based on environment characteristic detection, and aims to solve the problem that navigation semantic information is insufficient in positioning and is low in intelligence degree at present. The method comprises the following steps that: training an object detection model on a robot end, and establishing a semantic map which combines an indoor grid map with an object position; enabling a robot to rotate for one circle on the spot, utilizing the object detection model to identify an observed object, and utilizing a maximum likelihood estimation method to solve the global pose of the robot; and subscribing for the voice result topic of a remote control end by the robot end, carrying out mapping matching on an obtained voice result anda semantic dictionary in the semantic map, identifying a destination position to be navigated, and utilizing a center line in a corridor to plan the global path by the robot end. By use of the method,the problem that initial positioning can not be carried out in a traditional method is solved, the method is efficient and accurate in positioning, the planned path can more conform to the perceptionof the robot for an unknown environment so as to be safe, and realized semantic navigation can be more conveniently integrated in a robot product.

Description

technical field [0001] The invention belongs to the field of robot navigation, and relates to object detection technology, positioning navigation technology, central line path planning technology and the like. Background technique [0002] Localization is the premise and basis for mobile robots to carry out various navigation tasks. According to different task stages, positioning can be divided into global positioning and pose tracking. Global positioning refers to automatically calculating its own pose in the global map through its own sensors and algorithms without knowing the initial pose; Map, calculate the pose of the next cycle. [0003] Due to the lack of prior pose information for global positioning, the required observation information is more important. Therefore, it is often difficult to achieve autonomous global positioning due to the lack of semantic information of point cloud data observed by lidar. Most current global positioning algorithms are based on Vis...

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Application Information

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IPC IPC(8): G01C21/20G01S17/02
Inventor 董洪义王文奎丑武胜李宇航宋辉
Owner BEIHANG UNIV
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