LOCALISING TRANSPORTABLE APPARATUS

• US 20140233010A1
• Filed: 09/26/2012
• Published: 08/21/2014
• Est. Priority Date: 09/30/2011
• Status: Active Grant

• Current Assignee: Oxford University Innovation Limited (University of Oxford)
• Original Assignee: Chancellor Masters & Scholars of The University of Cambridge (University of Cambridge)
• Inventors: Baldwin, Ian Alan, Newman, Paul Michael
• Granted Patent: US 9,170,334 B2
• Time in Patent Office: Days
• US Class Current: 356/4.01
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01P 21/00 Testing or calibrating of apparatus or devices covered by the preceding groups
• CPC Class Codes: G01S 17/06 Systems determining position data of a target
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating

Abstract

A method of localising transportable apparatus (100) within an environment including receiving (402) data obtained from a ranging sensor device (102) of the transportable apparatus configured to collect information relating to at least a surface (120) over which the transportable apparatus is moving in an environment, and using (404) the ranging sensor device data to generate a new 3D point cloud (110) of the environment. The method further obtains (406) data representing an existing 3D point cloud (114) of at least part of the environment, and seeks to match (408) the new 3D point cloud with, or within, the existing 3D point cloud in order to localise the transportable apparatus with respect to the existing point cloud.

Claims

1. A method of localising transportable apparatus within an environment, the method including:

receiving data obtained from a ranging sensor device of the transportable apparatus configured to collect information relating to at least a surface over which the transportable apparatus is moving in an environment;

using the ranging sensor device data to generate a new 3D point cloud of the environment;

obtaining data representing an existing 3D point cloud of at least part of the environment; and

seeking to match the new 3D point cloud with, or within, the existing 3D point cloud in order to localise the transportable apparatus with respect to the existing point cloud.

2. A method according to claim 1, wherein the ranging sensor device comprises a LIDAR device.

3. A method according to claim 2, where, in use, a beam of the LIDAR device is aimed to continually strike the surface whilst the transportable apparatus is moving in use.

4. A method according to claim 2, wherein the LIDAR device is declined at an angle in a range of around 10°

to 90°

with respect to a main horizontal axis of the transportable apparatus.

5. A method according to claim 1, including reconstructing motion of the transportable apparatus over a period when the ranging sensor device data was created to generate the new 3D point cloud.

6. A method according to claim 5, including using information regarding linear and rotational velocities of the transportable apparatus provided during the period to generate the new 3D point cloud.

7. A method according to claim 6, wherein the ranging sensor device includes a clock configured to provide timing information relating to the information relating to at least the surface.

8. A method according to claim 7, wherein:

the information regarding rotational velocity is obtained from a rotational velocity measuring device, the rotational velocity measuring device including a clock to provide timing information relating, to the rotational velocity information; and

the information regarding linear velocity is obtained from a linear velocity measuring device configured to provide information relating to linear velocity of the transportable apparatus, the linear velocity measuring device including a clock to provide timing information relating to the linear velocity information; and

wherein the method includes calibrating the clock of the ranging sensor device, the clock of the linear velocity measuring device and the clock of the rotational velocity measuring device in order to coordinate information provided by those devices in terms of timing.

9. A method according to claim 8, wherein the calibrating involves computing probabilistic mappings between the clocks.

10. A method according to claim 8, including adjusting the velocity information provided by the linear velocity measuring device in view of a bias factor.

11. A method according, to claim 10, wherein computing the bias factor includes:

scaling observed velocity information over a test section of the environment;

generating a scaled 3D point cloud for the test section;

attempting to align the scaled 3D point cloud with a part of the existing 3D point cloud corresponding to the test section; and

using information relating to the attempted alignment to compute the bias factor.

12. A method according to claim 10, wherein computing:

of the bias factor includes;

analysing the ranging sensor data to identify a first set of repeated features in the environment;

analysing the existing 3D point loud data to identify a second set of repeated features;

computing a warping function to align the first set of repeated features and the second set of repeated features; and

using the warping function to determine the bias factor.

13. A method according to claim 12, wherein the repeated features comprise road markings.

15. Transportable apparatus including:

a ranging sensor device; and

a processor configured to execute a method according to claim 1.

14. (canceled)

16. A non-transient computer program product composing computer code that when executed by one or more processors causes a process for localizing a vehicle along a route to be carried out, the process comprising:

receiving data obtained from a ranging sensor device of the transportable apparatus configured to collect information relating to at least a surface over which the transportable apparatus is moving in an environment;

using the ranging sensor device data to generate a new 3D point cloud of the environment;

obtaining data representing an existing 3D point cloud of at least part of the environment; and

seeking to match the new 3D point cloud with, or within, the existing 3D point cloud in order to localise the transportable apparatus with respect to the existing point cloud.

17. A computer program product according to claim 16, the process further including:

reconstructing motion of the transportable apparatus over a period when the ranging sensor device data was created to generate the new 3D point cloud; and

using information regarding linear and rotational velocities of the transportable apparatus provided during the period to generate the new 3D point cloud;

wherein the ranging sensor device includes a clock configured to provide timing information relating to the information relating to at least the surface.

18. A computer program product according to claim 17, wherein:

the information regarding rotational velocity is obtained from a rotational velocity measuring device, the rotational velocity measuring, device including a clock to provide timing information relating to the rotational velocity information; and

the information regarding linear velocity is obtained from a linear velocity measuring device configured to provide information relating to linear velocity of the transportable apparatus, the linear velocity measuring device including a clock to provide timing information relating to the linear velocity information; and

wherein the process further includes calibrating the clock of the ranging, sensor device, the clock of the linear velocity measuring device and the clock of the rotational velocity measuring device in order to coordinate information provided by those devices in terms of timing; and

wherein the calibrating involves computing probabilistic mappings between the clocks.

19. A computer program product according to claim 18, the process further including adjusting the velocity information provided by the linear velocity measuring device in view of a bias factor.

20. A computer program product according to claim 19, wherein the bias factor is computed by:

scaling observed velocity information over a test section of the environment;

generating a scaled 3D point cloud for the test section;

attempting to align the scaled 3D point cloud with a part of the existing 3D point cloud corresponding to the test section; and

using information relating to the attempted alignment to compute the bias factor.

21. A computer program product according to claim 19, wherein the bias factor is computed by:

analysing the ranging sensor data to identify a first set of repeated features in the environment;

analysing the existing 3D point cloud data to identify a second set of repeated features;

computing a warping function to align the first set of repeated features and the second set of repeated features; and

using the warping function to determine the bias factor;

wherein the repeated features comprise road markings.