Indoor scene recognition method based on SVM and reinforcement learning

An indoor scene, reinforcement learning technology, applied in the field of indoor scene recognition, can solve problems such as huge workload, and achieve the effect of improving the accuracy rate and reducing the amount of calculation.

Inactive Publication Date: 2018-12-21
合肥中科自动控制系统有限公司
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Problems solved by technology

[0004] The purpose of the present invention is to provide an indoor scene recognition method based on SVM and reinforcement learning. In order to solve the problem that the existing technology can only rely on the richness of collected sample data to improve the recognition accuracy and the workload is huge

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  • Indoor scene recognition method based on SVM and reinforcement learning
  • Indoor scene recognition method based on SVM and reinforcement learning
  • Indoor scene recognition method based on SVM and reinforcement learning

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Embodiment Construction

[0050] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. This embodiment is only used to explain the present invention and does not limit the protection scope of the present invention.

[0051] This embodiment proposes an indoor scene recognition method based on SVM and reinforcement learning, the method includes the following steps:

[0052] (1) Indoor scene collection data, training a SVM classifier N capable of indoor scene classification s , assuming that there are four types of scenarios, respectively set to 1, 2, 3, 4, the specific steps are as follows:

[0053] (1-1) Collect lidar data in indoor scenes and name it as data set D. The specific steps are as follows:

[0054] (1-1-1) Set multiple collection points for each indoor scene, collect data, including data and tags, and the tags are room categories, which are 1, 2, 3, and 4 re...

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Abstract

The invention discloses an indoor scene recognition method based on SVM and reinforcement learning. The method comprises the following steps: (1) collecting data of the indoor scene and training an SVM classifier Ns capable of classifying the indoor scene; (2) Conducting several indoor scene active recognition experiments. In the experiment, a reinforcement learning neural network fitting the reinforcement learning value function is trained, and the network is named decision network NQ. The decision network NQ judges the classification result by Ns. (3) After the training of decision network NQ, Decision-making network NQ is used to make decision and execute the robot action according to the room laser ranging information currently obtained by the laser ranging sensor. After the action isexecuted, the information of the laser ranging sensor is collected again and inputted into NQ. Thus, the result obtained three times is fused to obtain the final classification result. In this method,the laser ranging information is transformed into the projection information of the scene contour map ring and the SVM is used for scene recognition, which reduces the computational load and improvesthe correct rate of scene recognition.

Description

technical field [0001] The invention relates to an indoor scene recognition method, in particular to an indoor scene recognition method based on SVM and reinforcement learning. Background technique [0002] In recent years, robotic scene recognition has been used in an increasing number of robot localization functions. The robot recognizes the type of environment as soon as possible in an unfamiliar environment, laying the foundation for it to realize various other functions such as path planning and behavior control. In the current application scenario, the laser sensor equipped on the robot can only obtain the distance information of 180° ahead. Due to the limitation of the robot's orientation and the data limitation of the low-cost sensor, the accuracy of scene recognition is low. [0003] There is an invention patent application document. The subject of the patent application is "A Scene Recognition Method Based on Single Hidden Layer Neural Network". The application pu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/2411
Inventor 黄学艺刘华平宋彦袁胜赵江海
Owner 合肥中科自动控制系统有限公司
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