Autonomous vehicle, and object recognizing method in autonomous vehicle
First Claim
1. An unmanned forklift comprising:
- a pair of forks disposed on a front surface of the unmanned forklift for insertion into a pallet that includes inserting ports;
a travel vehicle main body that autonomously travels to a target position based on travel control information;
a model data storage that stores model data related to a geometric feature of the pallet;
a photographic device that photographs a periphery of the travel vehicle main body at the target position to acquire image data; and
a controller;
whereinthe controller receives the travel control information including a travel path from a current position to the target position and a position information of the pallet;
the controller calculates current self-posture information of the unmanned forklift based on the travel control information;
the controller predicts a position of the pallet in a warehouse based on the image data, and determines a search region of a predetermined range that corresponds to a portion of the warehouse that includes the predicted position of the pallet within the warehouse based on the position information of the pallet and the current self-posture information;
the controller detects a feature point of the image data with respect to the search region;
the controller calculates a feature amount of a matching candidate point which is extracted from the feature point; and
the controller matches the feature amount of the matching candidate point with the model data to recognize the position of the pallet based on the image data.
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Accused Products
Abstract
An autonomous vehicle includes a travel vehicle main body, a model data storage, a photographic device, a search region determiner, an image feature point detector, a feature amount calculator and a position detector. The travel vehicle main body autonomously travels to a target position. The model data storage stores model data related to a geometric feature of an object. The photographic device photographs a periphery of the travel vehicle main body at the target position to acquire image data. The search region determiner predicts a position of the object based on the image data, and determines a search region of a predetermined range including the predicted position of the object. The image feature point detector detects a feature point of the image data with respect to the search region. The feature amount calculator calculates a feature amount of a matching candidate point extracted from the feature point. The position detector matches the feature amount of the matching candidate point with the model data to recognize the position of the object based on the image data.
21 Citations
16 Claims
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1. An unmanned forklift comprising:
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a pair of forks disposed on a front surface of the unmanned forklift for insertion into a pallet that includes inserting ports; a travel vehicle main body that autonomously travels to a target position based on travel control information; a model data storage that stores model data related to a geometric feature of the pallet; a photographic device that photographs a periphery of the travel vehicle main body at the target position to acquire image data; and a controller;
whereinthe controller receives the travel control information including a travel path from a current position to the target position and a position information of the pallet; the controller calculates current self-posture information of the unmanned forklift based on the travel control information; the controller predicts a position of the pallet in a warehouse based on the image data, and determines a search region of a predetermined range that corresponds to a portion of the warehouse that includes the predicted position of the pallet within the warehouse based on the position information of the pallet and the current self-posture information; the controller detects a feature point of the image data with respect to the search region; the controller calculates a feature amount of a matching candidate point which is extracted from the feature point; and the controller matches the feature amount of the matching candidate point with the model data to recognize the position of the pallet based on the image data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A pallet recognizing method for use with an unmanned forklift in a warehouse, the unmanned forklift including a pair of forks disposed on a front surface of the unmanned forklift and a travel vehicle main body, the pallet recognizing method comprising the steps of:
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transmitting travel control information including a travel path from a current position to a target position and a position information of a pallet; calculating current self-posture information of the unmanned forklift based on the travel control information; causing the unmanned forklift to autonomously travel to the target position based on the travel control information; storing model data related to a geometric feature of the pallet; photographing a periphery of the travel vehicle main body at the target position to acquire image data; predicting a position of the pallet based on the image data; determining a search region of a predetermined range that corresponds to a portion of the warehouse that includes the predicted position of the pallet within the warehouse based on the position information of the pallet and the current self-posture information; detecting a feature point of the image data with respect to the search region; calculating a feature amount of a matching candidate points that is extracted from the feature point; and matching the feature amount of the matching candidate points with the model data to recognize the position of the pallet based on the image data.
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Specification