Vehicle positioning or navigation utilizing associated feature pairs

• US 9,984,467 B2
• Filed: 06/26/2015
• Issued: 05/29/2018
• 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): Antonucci, Anne M
• Assistant Examiner(s): Malhotra, Sanjeev
• Application Number:
  US14/751,375
• Publication Number:
  US 20150379715A1
• Time in Patent Office: 1,068 Days
• Field of Search: 701216, 701218, 701228, 701230, 701533, 701523, 37524022, 340 101, 198431, 41422207, 73146, 707738, 382103, 345419
• 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

According to the embodiments described herein, 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 vehicular processor(s) and captures an input image of overhead features;

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

the vehicular processor(s) of the materials handling vehicle executes vehicle functions to;

(i) create a plurality of overhead associated feature pairs by;

retrieving an initial input image comprising overhead features from the camera comprising two-dimensional UV space information wherein U and V each correspond to a dimension in a two-dimensional space;

forming a plurality of overhead feature pairs by pairing points from the overhead features in the two-dimensional UV space information in the input image; and

associating the plurality of overhead feature pairs from the two-dimensional UV space information with overhead global feature point pairs formed from pairing the plurality of three-dimensional overhead global feature points of the industrial facility map, wherein a first overhead feature pair of the plurality of overhead feature pairs from the two-dimensional UV space information is associated with a first overhead global feature point pair formed from pairing a first pair of three-dimensional overhead global feature points such that the first overhead feature pair is associated with the first overhead global feature point pair to form a first overhead associated feature pair of the plurality of overhead associated feature pairs;

(ii) calculate a vehicle pose from the each of the plurality of overhead associated feature pairs within a visual range of the camera;

(iii) calculate a best estimate pose of the materials handling vehicle based on a highest match-rated pair of the plurality of overhead associated feature pairs by ranking matches between the plurality of overhead feature pairs from the two-dimensional UV space information and corresponding overhead global feature point pairs formed from the industrial facility map;

(iv) use an accumulated odometry of the materials handling vehicle to update the best estimate pose to a current localized position of the materials handling vehicle; and

(v) utilize the current localized position to;

track the navigation of the materials handling vehicle along the inventory transit surface, or navigate the materials handling vehicle along the inventory transit surface in at least a partially automated manner, or both.

2. The materials handling vehicle of claim 1, wherein the vehicular processor(s) further executes vehicle functions to validate the current localized position prior to updating the current localized position, such validation comprising functions to:

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

3. The materials handling vehicle of claim 1, wherein the vehicular processor(s) further executes vehicle functions to publish the current localized position on a display after updating the current localized position.

4. The materials handling vehicle of claim 1, wherein the vehicular processor(s) further executes vehicle functions to:

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

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

5. The materials handling vehicle of claim 4, wherein determine if the materials handling vehicle is lost comprises determine if the materials handling vehicle is lost based on whether the current localized position comprises incorrect data.

6. The materials handling vehicle of claim 4, the determination of whether the materials handling vehicle is lost is based at least in part on whether the camera data is insufficient to correct for an error associated with the accumulated odometry.

7. The materials handling vehicle of claim 4, wherein the plurality of overhead 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.

8. 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:

(i) N is a cross product of two 3D vectors;

(ii) T is a target 3D point for yaw rotation;

9. The materials handling vehicle of claim 8, wherein the calculation of a best estimate pose of the materials handling vehicle based on a highest-rated pair of the plurality of overhead associated feature pairs is based at least in part on a calculation of a pose sum error for each calculated vehicle pose wherein the highest-rated pair has a lowest pose sum error.

10. 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 bay of an industrial facility, and vehicle control architecture in communication with the drive and materials handling mechanisms;

the camera is communicatively coupled to the vehicular processor(s) and the management server and captures an input image of overhead features;

the vehicular processor(s) is communicatively coupled to the management server;

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

the vehicular processor(s) of the materials handling vehicle executes vehicle functions to;

(i) create a plurality of overhead associated feature pairs by;

retrieving an initial input image comprising overhead features from the camera comprising two-dimensional UV space information wherein U and V each correspond to a dimension in a two-dimensional space;

forming a plurality of overhead feature pairs by pairing points from the overhead features in the two-dimensional UV space information in the input image; and

associating the plurality of overhead feature pairs from the two-dimensional UV space information with overhead global feature point pairs formed from pairing the plurality of three-dimensional overhead global feature points of the industrial facility map, wherein a first overhead feature pair of the plurality of overhead feature pairs from the two-dimensional UV space information is associated with a first overhead global feature point pair formed from pairing a first pair of three-dimensional overhead global feature points such that the first overhead feature pair is associated with the first overhead global feature point pair to form a first overhead associated feature pair of the plurality of overhead associated feature pairs;

(ii) calculate a vehicle pose from each of the plurality of overhead associated feature pairs within a visual range of the camera;

(iii) calculate a best estimate pose of the materials handling vehicle based on a highest match-rated pair of the plurality of overhead associated feature pairs by ranking matches between the plurality of overhead feature pairs from the two-dimensional UV space information and corresponding overhead global feature point pairs formed from the industrial facility map;

(iv) use an accumulated odometry of the materials handling vehicle to update the best estimate pose to a current localized position of the materials handling vehicle; and

(v) utilize the current localized position to;

track the navigation of the materials handling vehicle along the inventory transit surface, or navigate the materials handling vehicle along the inventory transit surface in at least a partially automated manner, or both.

11. The system of claim 10, wherein the vehicular processor(s) further executes vehicle functions to validate the current localized position prior to updating the current localized position, such validation comprising functions to:

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

12. The system of claim 10, wherein the vehicular processor(s) further executes vehicle functions to publish the current localized position on a display after updating the current localized position.

13. The system of claim 10, wherein the vehicular processor(s) further executes vehicle functions to:

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

(ii) if the materials handling vehicle is determined to be lost, retrieve a subsequent set of camera data from the camera and repeat functions (i)-(v) of claim 10 with the subsequent set of camera data from the camera in place of an initial 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 at least in part on whether the current localized position comprises incorrect data.

15. The system of claim 13, wherein the determination of whether the materials handling vehicle is lost is based at least in part on whether the camera data is insufficient to correct for an error associated with the accumulated odometry.

16. The system of claim 13, wherein the plurality of overhead 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.

17. The system of claim 10, 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:

(i) N is a cross product of two 3D vectors;

(ii) T is a target 3D point for yaw rotation;

18. The system of claim 17, wherein the calculation of a best estimate pose of the materials handling vehicle based on a highest-rated pair of the plurality of overhead associated feature pairs is based at least in part on a calculation of a pose sum error for each calculated vehicle pose wherein the highest-rated pair has a lowest pose sum error.

19. A method of navigating or tracking the 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 overhead global feature points associated with a mapping of the ceiling lights;

creating a plurality of overhead associated feature pairs by;

retrieving an initial input image comprising overhead features from the camera comprising two-dimensional UV space information, U and V each correspond to a dimension in a two-dimensional space;

forming a plurality of overhead feature pairs by pairing points from the overhead features in the two-dimensional UV space information in the input image; and

associating the plurality of overhead feature pairs from the two-dimensional UV space information with overhead global feature point pairs formed from pairing the plurality of three-dimensional overhead global feature points of the industrial facility map, wherein a first overhead feature pair of the plurality of overhead feature pairs from the two-dimensional UV space information is associated with a first overhead global feature point pair formed from pairing a first pair of three-dimensional overhead global feature points such that the first overhead feature pair is associated with the first overhead global feature point pair to form a first overhead associated feature pair of the plurality of overhead associated feature pairs;

via the vehicular processor(s), calculating a vehicle pose from each of the plurality of overhead associated feature pairs within a visual range of the camera;

via the vehicular processor(s), calculating a best estimate pose of the materials handling vehicle based on a highest match-rated pair of the plurality of overhead associated feature pairs by ranking matches between the plurality of overhead feature pairs from the two-dimensional UV space information and corresponding overhead global feature point pairs formed from the industrial facility map;

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

via the vehicular processor(s) and the management server, utilizing the current localized position to;

track the navigation of the materials handling vehicle along the inventory transit surface, or navigate the materials handling vehicle along the inventory transit surface in at least a partially automated manner, or both.

20. The method of claim 19, further comprising:

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

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

21. The method of claim 19, further comprising:

publishing the current localized position on a display after updating the current localized position.

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

determining whether the materials handling vehicle is lost;

if 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 two-dimensional UV space information, and associating each pair from the two-dimensional UV space information with pairs from each of the plurality of three-dimensional overhead global feature points of the industrial facility map to create a plurality of new overhead associated feature pairs;

via the vehicular processor(s), calculating a vehicle pose for each new associated feature pair within a visual range of the camera;

via the vehicular processor(s), calculating a new best estimate pose of the materials handling vehicle based on a highest-rated pair of the plurality of new overhead 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 new current localized position.

23. The method of claim 22, wherein the determination of whether the materials handling vehicle is lost is based at least in part on whether the current localized position comprises incorrect data.

24. The method of claim 22, wherein the determination of whether the materials handling vehicle is lost is based at least in part on whether the camera data is insufficient to correct for an error associated with the accumulated odometry.

25. The method of claim 22, wherein the plurality of overhead 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.

26. The method of claim 19, 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 using 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:

(i) N is a cross product of two 3D vectors;

(ii) T is a target 3D point for yaw rotation;

27. The method of claim 26, wherein the calculation of a best estimate pose of the materials handling vehicle based on a highest-rated pair of the plurality of overhead associated feature pairs is based at least in part on calculating a pose sum error for each calculated vehicle pose wherein the highest-rated pair has a lowest pose sum error.