AUTONOMOUS VEHICLE, AND OBJECT RECOGNIZING METHOD IN AUTONOMOUS VEHICLE

• US 20150347840A1
• Filed: 03/03/2015
• Published: 12/03/2015
• Est. Priority Date: 05/27/2014
• Status: Active Grant

• Current Assignee: Murata Machinery Limited (Muratec)
• Original Assignee: Murata Machinery Limited (Muratec)
• Inventors: IIDA, Masaomi
• Granted Patent: US 9,635,346 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B66F 9/063 Automatically guided
• CPC Class Codes: G01B 11/24 for measuring contours or curvatures
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/894 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• CPC Class Codes: G01S 7/4802 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G05D 1/0225 involving docking at a fixed facility, e.g. base station or loading bay parking aids B62D15/027
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06T 2207/10012 Stereo images
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/593 from stereo images
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 20/64 Three-dimensional objects
• CPC Class Codes: G06V 20/653 by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
• CPC Class Codes: G06V 20/80 Recognising image objects characterised by unique random patterns
• CPC Class Codes: H04N 13/204 using stereoscopic image cameras stereoscopic photography G03B35/00
• CPC Class Codes: H04N 13/239 using two 2D image sensors having a relative position equal to or related to the interocular distance H04N13/243 takes precedence
• CPC Class Codes: H04N 13/271 wherein the generated image signals comprise depth maps or disparity maps

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.

Claims

1. An autonomous vehicle comprising:

a travel vehicle main body configured to autonomously travel to a target position;

a model data storage configured to store model data related to a geometric feature of an object;

a photographic device configured to photograph a periphery of the travel vehicle main body at the target position to acquire image data;

a search region determiner configured to predict a position of the object based on the image data, and determine a search region of a predetermined range including the predicted position of the object;

an image feature point detector configured to detect a feature point of the image data with respect to the search region;

a feature amount calculator configured to calculate a feature amount of a matching candidate point extracted from the feature point; and

a position detector configured to match the feature amount of the matching candidate point with the model data to recognize the position of the object based on the image data.

2. The autonomous vehicle according to claim 1, further comprising:

a posture calculator configured to calculate current posture information;

wherein the search region determiner predicts the position of the object based on the current posture information.

3. The autonomous vehicle according to claim 1, wherein the position detector extracts a plurality of feature points satisfying a specific geometric condition from among the feature points as the matching candidate points.

4. The autonomous vehicle according to claim 3, wherein when some of the plurality of feature points satisfying the specific geometric condition do not exist in the search region, the search region determiner enlarges the search region to the periphery to detect the feature point.

5. The autonomous vehicle according to claim 4, wherein when a similarity degree with respect to the model data of the matching candidate point existing in the search region is low, the search region determiner enlarges the search region to the periphery and the position detector again extracts the matching candidate point.

6. The autonomous vehicle according to claim 3, wherein

the object is a pallet including three perpendicular or substantially perpendicular planes and insertion ports to which forks are inserted;

and

the position detector is configured to extract, as matching candidate points, three feature points appearing at an edge where the three perpendicular or substantially perpendicular planes of the pallet make contact with a floor surface.

7. The autonomous vehicle according to claim 6, wherein the geometric condition is defined by a distance between a first feature point and a second feature point, a distance between the first feature point and a third feature point, and a distance between the second feature point and the third feature point, the first feature point being a feature point positioned at a middle of the three feature points, and the second feature point and the third feature point being feature points on both sides of the first feature point.

8. The autonomous vehicle according to claim 7, wherein the geometric condition includes an angle defined by a line segment connecting the first feature point and the second feature point and a line segment connecting the first feature point and the third feature point.

9. The autonomous vehicle according to claim 3, wherein the position detector is configured to select one or more interpolation points satisfying a predetermined geometric condition with respect to the plurality of feature points, and to extract the plurality of feature points and the one or more interpolation points as the matching candidate points.

10. The autonomous vehicle according to claim 1, wherein the image data is three-dimensional image data.

11. The autonomous vehicle according to claim 10, wherein the photographic device is a stereo camera including two cameras configured to capture two-dimensional images, a feature point on the two-dimensional image is obtained based on the two two-dimensional images captured with the two cameras, and a distance image includes distance data captured with a distance sensor with respect to each feature point.

12. The autonomous vehicle according to claim 10, wherein the photographic device is a Time Of Flight camera configured to radiate an infrared light using an LED disposed at a periphery of the camera, to measure a time until a reflected light reflected from the object is observed with the camera, and to measure a distance to the object.

13. The autonomous vehicle according to claim 3, wherein the autonomous vehicle is an unmanned forklift including a pair of forks disposed on a front surface, and the photographic device is configured to photograph a front of the pair of forks.

14. The autonomous vehicle according to claim 13, wherein the photographic device is disposed at a front end of one of the pair of forks.

15. The autonomous vehicle according to claim 1, wherein the image feature point detector is configured to extract a point where color and shading change as a feature point for each pixel of the image data.

16. The autonomous vehicle according to claim 15, wherein the image feature point detector is configured to extract a feature point using a Harris operator.

17. An object recognizing method in an autonomous vehicle including a travel vehicle main body, the object recognizing method comprising the steps of:

autonomously travelling to a target position;

storing model data related to a geometric feature of an object;

photographing a periphery of the travel vehicle main body at the target position to acquire image data;

predicting a position of the object based on the image data and determining a search region of a predetermined range including the predicted position of the object;

detecting a feature point of the image data with respect to the search region;

calculating a feature amount of matching candidate points 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 object based on the image data.