Vehicle positioning or navigation utilizing associated feature pairs

• US 10,614,588 B2
• Filed: 04/27/2018
• Issued: 04/07/2020
• Est. Priority Date: 06/27/2014
• Status: Active Grant

• Current Assignee: Crown Equipment Corporation
• Original Assignee: Crown Equipment Corporation
• Inventors: Chandrasekar, Kashyap, Estep, Ryan, Thomson, Jacob, Waltz, Lucas
• Primary Examiner(s): Jimenez, Anthony R
• Application Number:
  US15/964,192
• Publication Number:
  US 20180247428A1
• Time in Patent Office: 711 Days
• Field of Search: 701 22
• CPC Class Codes: G01B 11/14 for measuring distance or clearance between spaced objects or spaced apertures G01B11/26 takes precedence; rangefinders G01C3/00
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0272 comprising means for registering the travel distance, e.g. revolutions of wheels measuring distance traversed on the ground by vehicles, e.g. using odometers G01C22/00
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30108 Industrial image inspection
• CPC Class Codes: G06T 2207/30248 Vehicle exterior or interior
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 30/422 Technical drawings; Geographical maps

Abstract

A method for vehicle positioning or navigation utilizing associated feature pairs may include creating a plurality of associated feature pairs by retrieving an initial set of camera data from the camera comprising two-dimensional UV space information, forming pairs from the UV space information, and associating each pair from the UV space information with pairs from each of the plurality of three-dimensional global feature points of the industrial facility map, calculating a best estimate poses from calculated vehicle poses of the associated feature pairs, using an accumulated odometry to update the best estimate pose to a current localized position and setting a seed position as the current localized position. The navigation of the materials handling vehicle is tracked and/or navigated along the inventory transit surface navigate in at least a partially automated manner utilizing the current localized position.

Claims

1. A materials handling vehicle comprising a camera, one or more vehicular processors, a drive mechanism configured to move the materials handling vehicle along an inventory transit surface, a materials handling mechanism configured to store and retrieve goods in a storage bay of an industrial facility, and vehicle control architecture in communication with the drive and materials handling mechanisms, wherein:

the camera is communicatively coupled to the one or more vehicular processors and captures an input image of overhead features;

an industrial facility map comprises a plurality of three-dimensional global feature points associated with a mapping of the overhead features; and

the one or more vehicular processors of the materials handling vehicle execute vehicle functions to;

(i) create a plurality of associated feature pairs by retrieving a set of camera data from the camera comprising two-dimensional UV space information, forming pairs from the UV space information, and mapping pairs from the UV space information with pairs from the plurality of three-dimensional global feature points of the industrial facility map;

(ii) calculate respective vehicle poses for associated feature pairs within a visual range of the camera;

(iii) determine a smallest pose sum error by comparing a plurality of pose sum errors of the plurality of associated feature pairs;

(iv) calculate a best estimate pose of the materials handling vehicle based on a highest-ranked pair of the plurality of associated feature pairs, wherein the highest-ranked pair comprises the smallest pose sum error of the plurality of associated feature pairs; and

(v) navigate the materials handling vehicle utilizing the best estimate pose.

2. The materials handling vehicle of claim 1, wherein the plurality of associated feature pairs are created by data association steps comprising:

(i) creating UV space information pairs;

(ii) associating each global feature point with a first UV space feature in each UV space information pair; and

(iii) associating each global feature point within a maximum visual range of the camera with a second UV space feature in each UV space information pair.

3. The materials handling vehicle of claim 1, wherein the calculation of a vehicle pose for each associated feature pair within a visual range of the camera is based at least in part on use of a global localization algorithm, wherein the global localization algorithm comprises a formula to calculate a yaw of the vehicle about a first UV space feature of an associated feature pair that comprises an anchored feature, wherein the formula comprises:

4. The materials handling vehicle of claim 1, wherein the one or more vehicular processors further execute vehicle functions to:

(i) use an accumulated odometry associated with the materials handling vehicle to update the best estimate pose to a current localized position;

(ii) set a seed position as the current localized position;

(iii) track the navigation of the materials handling vehicle along the inventory transit surface, navigate the materials handling vehicle along the inventory transit surface in at least a partially automated manner, or both, utilizing the current localized position.

5. The materials handling vehicle of claim 4, wherein the one or more vehicle processors further execute vehicle functions to validate the current localized position prior to updating the seed position as the current localized position, such validation comprising functions to:

(i) record the number of unique global feature points after each data association step until the number is above a threshold such that the seed position as the current localized position is validated.

6. The materials handling vehicle of claim 4, wherein the one or more vehicular processors further execute vehicle functions to publish the seed position as the current localized position on a display after updating the seed position as the current localized position.

7. The materials handling vehicle of claim 1, wherein the one or more vehicular processors further executes vehicle functions to:

(i) determine when the materials handling vehicle is lost;

(ii) when the materials handling vehicle is determined to be lost, retrieve a subsequent set of camera data from the camera and repeat functions (i)-(iv) of claim 1 with the subsequent set of camera data from the camera in place of the set of camera data from the camera until the materials handling vehicle is found.

8. The materials handling vehicle of claim 7, wherein determine when the materials handling vehicle is lost comprises determine when the materials handling vehicle is lost based on at least one of:

(i) whether a seed position comprises incorrect data; and

(ii) whether the camera data is insufficient to correct for an error associated with an accumulated odometry associated with the materials handling vehicle.

9. A system comprising a materials handling vehicle and a management server, wherein:

the materials handling vehicle comprises a camera, one or more vehicular processors, a drive mechanism configured to move the materials handling vehicle along an inventory transit surface, a materials handling mechanism configured to store and retrieve goods in a storage hay of an industrial facility, and vehicle control architecture in communication with the drive and materials handling mechanisms;

the camera is communicatively coupled to the one or more vehicular processors and the management server and captures an input image of overhead features;

the one or more vehicular processors are communicatively coupled to the management server;

an industrial facility map stored on the management server is communicatively coupled to the one or more vehicular processors, wherein the industrial facility map comprises a plurality of three-dimensional global feature points associated with a mapping of the overhead features; and

the one or more vehicular processors of the materials handling vehicle executes vehicle functions to;

(i) create a plurality of associated feature pairs by retrieving a set of camera data from the camera comprising two-dimensional UV space information, forming pairs from the UV space information, and mapping pairs from the UV space information with pairs from the plurality of three-dimensional global feature points of the industrial facility map;

(ii) calculate respective vehicle poses for associated feature pairs within a visual range of the camera;

(iii) determine a smallest pose sum error by comparing a plurality of pose sum errors of the plurality of associated feature pairs;

(iv) calculate a best estimate pose of the materials handling vehicle from the calculated vehicle poses based on a highest-ranked pair of the plurality of associated feature pairs, wherein the highest-ranked pair comprises the smallest pose sum error of the plurality of associated feature pairs; and

(v) navigate the materials handling vehicle utilizing the best estimate pose.

10. The system of claim 9, wherein the plurality of associated feature pairs are created by data association steps comprising:

(i) creating UV space information pairs;

(ii) associating each global feature point with a first UV space feature in each UV space information pair; and

(iii) associating each global feature point within a maximum visual range of the camera with a second UV space feature in each UV space information pair.

11. The system of claim 9, wherein the one or more vehicular processors further execute vehicle functions to:

(i) use an accumulated odometry associated with the materials handling vehicle to update the best estimate pose to a current localized position;

(ii) set a seed position as the current localized position;

(iii) publish the seed position as the current localized position on a display; and

(iv) track the navigation of the materials handling vehicle along the inventory transit surface, navigate the materials handling vehicle along the inventory transit surface in at least a partially automated manner, or both, utilizing the current localized position.

12. The system of claim 11;

wherein the one or more vehicular processors further execute vehicle functions to validate the current localized position prior to updating the seed position as the current localized position, such validation comprising functions to;

(i) record the number of unique global feature points after each data association step until the number is above a threshold such that the seed position as the current localized position is validated.

13. The system of claim 9, wherein the one or more vehicular processors further execute vehicle functions to:

(i) determine when the materials handling vehicle is lost; and

(ii) when the materials handling vehicle is determined to be lost, retrieve a subsequent set of camera data from the camera and repeat functions (i)-(iv) of claim 9 with the subsequent set of camera data from the camera in place of the set of camera data from the camera until the materials handling vehicle is found.

14. The system of claim 13, wherein the determination of whether the materials handling vehicle is lost is based on at least one of:

(i) whether the seed position comprises incorrect data; and

(ii) whether the camera data is insufficient to correct for an error associated with the accumulated odometry.

15. A method of navigating or tracking a navigation of a materials handling vehicle along an inventory transit surface, the method comprising:

disposing a materials handling vehicle upon an inventory transit surface of an industrial facility, wherein the materials handling vehicle is in communication with a management server, wherein the materials handling vehicle comprises a camera, one or more vehicular processors, a drive mechanism configured to move the materials handling vehicle along the inventory transit surface, a materials handling mechanism configured to store and retrieve goods in a storage bay of the industrial facility, and vehicle control architecture in communication with the drive and materials handling mechanisms;

utilizing the drive mechanism to move the materials handling vehicle along the inventory transit surface;

capturing an input image of overhead features of the industrial facility via the camera as the materials handling vehicle moves along the inventory transit surface;

providing an industrial facility map comprising a plurality of three-dimensional global feature points associated with a mapping of the overhead features;

creating a plurality of associated feature pairs by retrieving a set of camera data from the camera comprising two-dimensional UV space information, forming pairs from the UV space information, and mapping pairs from the UV space information with pairs from the plurality of three-dimensional global feature points of the industrial facility map;

via the one or more vehicular processors, calculating respective vehicle poses for associated feature pairs within a visual range of the camera;

via the one or more vehicular processors, determining a smallest pose sum error by comparing a plurality of pose sum errors of the plurality of associated feature pairs;

via the one or more vehicular processors, calculating a best estimate pose of the materials handling vehicle from the calculated vehicle poses based on a highest-ranked pair of the plurality of associated feature pairs, wherein the highest-ranked pair comprises the smallest pose sum error of the plurality of associated feature pairs; and

navigating the materials handling vehicle along the inventory transit surface utilizing the best estimate pose.

16. The method of claim 15, wherein the plurality of associated feature pairs are created by data association steps comprising:

creating UV space information pairs;

associating each global feature point with a first UV space feature in each UV space information pair; and

associating each global feature point within a maximum visual range of the camera with a second UV space feature in each UV space information pair.

17. The method of claim 15, further comprising:

using an accumulated odometry associated with the materials handling vehicle to update the best estimate pose to a current localized position;

setting a seed position as the current localized position;

publishing the seed position as the current localized position on a display; and

via the one or more vehicular processors and the management server, tracking the navigation of the materials handling vehicle along the inventory transit surface, navigating the materials handling vehicle along the inventory transit surface in at least a partially automated manner, or both, utilizing the current localized position.

18. The method of claim 17, further comprising:

validating the current localized position prior to updating the seed position as the current localized position, such validation comprising;

recording the number of unique global feature points after each data association step until the number is above a threshold such that the seed position as the current localized position is validated.

19. The method of claim 15 the method further comprising:

determining whether the materials handling vehicle is lost;

when the materials handling vehicle is determined to be lost, finding the materials handling vehicle by retrieving a subsequent set of camera data from the camera comprising two-dimensional UV space information, forming pairs from the UV space information, and mapping each pair from the UV space information with pairs from each of the plurality of three-dimensional global feature points of the industrial facility map to create a plurality of new associated feature pairs;

via the one or more vehicular processors, calculating a respective vehicle pose for each new associated feature pair within a visual range of the camera;

via the one or more vehicular processors, calculating a new best estimate pose of the materials handling vehicle from the calculated vehicle poses based on a highest-ranked pair of the plurality of new associated feature pairs;

using an accumulated odometry associated with the materials handling vehicle to update the new best estimate pose to a new current localized position; and

setting the seed position as the new current localized position.

20. The method of claim 19, wherein the determination of whether the materials handling vehicle is lost is based on at least one of:

(i) whether the seed position comprises incorrect data; and

(ii) whether the camera data is insufficient to correct for an error associated with the accumulated odometry.