METHOD AND APPARATUS FOR INTENSITY CALIBRATION OF MULTI-BEAM LIDAR SENSORS

• US 20140152971A1
• Filed: 12/05/2012
• Published: 06/05/2014
• Est. Priority Date: 12/05/2012
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: JAMES, Michael R.
• Granted Patent: US 9,046,600 B2
• Time in Patent Office: Days
• US Class Current: 356/4.01
• CPC Class Codes: G01C 15/002 Active optical surveying means optical plumbing G01C15/105
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating

Abstract

A method of multi-beam Lidar intensity measurement calibration including determining, by a positioning device, position information of a moving platform within a global reference frame, collecting a plurality of data points, each data point including the position information and intensity information from a plurality of beams, segmenting the plurality of data points into a plurality of voxels, based on the position information associated with each data point, finding a subset of voxels in the plurality of voxels that include a point corresponding to a certain beam in the plurality of beams and an intensity value in a predetermined neighborhood of a certain intensity value, estimating a probability density based on the subset of voxels, and generating a calibration map that maps the certain intensity value of the certain beam to a calibrated intensity value for the certain beam, based on the probability density and a prior density.

Claims

1. A method of multi-beam Lidar intensity measurement calibration comprising:

determining, by a positioning device, position information of a moving platform within a global reference frame;

collecting a plurality of data points, each data point including the position information and intensity information from a plurality of beams;

segmenting the plurality of data points into a plurality of voxels, based on the position information associated with each data point;

finding a subset of voxels in the plurality of voxels that include a point corresponding to a certain beam in the plurality of beams and an intensity value in a predetermined neighborhood of a certain intensity value;

estimating a probability density based on the subset of voxels; and

generating a calibration map that maps the certain intensity value of the certain beam to a calibrated intensity value for the certain beam, based on the probability density and a prior density.

2. The method of claim 1, wherein estimating the probability density comprises:

finding an average intensity value for each voxel in the subset of voxels;

defining, for each voxel in the subset of voxels, a kernel with a predetermined bandwidth and a mean equal to the average intensity value for that voxel; and

finding, for each intensity value in the predetermined neighborhood of the intensity values, the probability density generated by the kernels of the voxels corresponding to that intensity value.

3. The method of claim 2, wherein estimating the probability density further comprises:

finding a weighted average of average kernels of the subset of voxels, based on a weighting kernel function with a predetermined weighting bandwidth and a weighting mean equal to the certain intensity value.

4. The method of claim 1, wherein the prior density function is a Gaussian density function.

5. The method of claim 1, wherein the calibration map is a maximum likelihood calibration map.

6. The method of claim 1, wherein the positioning device includes one or more of a wheel odometry device, an inertial measurement device, or a global positioning system (GPS).

7. An apparatus for multi-beam Lidar intensity measurement calibration comprising:

a data acquisition unit which collects a plurality of data points, each data point including position information of a moving platform and intensity information from a plurality of beams;

a processor configured to

segment the plurality of data points into a plurality of voxels, based on the position information associated with each data point,

find a subset of voxels in the plurality of voxels that include a point corresponding to a certain beam in the plurality of beams and an intensity value in a predetermined neighborhood of a certain intensity value;

estimate a probability density based on the subset of voxels; and

generate a calibration map that maps the certain intensity value of the certain beam to a calibrated intensity value for the certain beam, based on the probability density and a prior density.

8. The apparatus of claim 7, wherein the processor is further configured to

find an average intensity value for each voxel in the subset of voxels;

define, for each voxel in the subset of voxels, a kernel with a predetermined bandwidth and a mean equal to the average intensity value for that voxel; and

find, for each intensity value in the predetermined neighborhood of the intensity values, a probability density generated by the kernels of the voxels corresponding to that intensity value.

9. The apparatus of claim 8, wherein the processor is further configured to find a weighted average of average kernels of the subset of voxels, based on a weighting kernel function with a predetermined weighting bandwidth and a weighting mean equal to the certain intensity value.

10. The apparatus of claim 7, wherein the processor uses a Gaussian density function as the prior density function.

11. The apparatus of claim 7, wherein the processor generates a maximum likelihood calibration map as the calibration map.

12. The apparatus of claim 7, wherein the position information is based on one or more of a wheel odometry device, an inertial measurement device, or a global positioning system (GPS).

13. A non-transitory computer-readable medium storing a program that, when executed by a processor, causes the processor to perform a method of multi-beam Lidar intensity measurement calibration comprising:

determining, by a positioning device, position information of a moving platform within a global reference frame;

collecting a plurality of data points, each data point including the position information and intensity information from a plurality of beams;

segmenting the plurality of data points into a plurality of voxels, based on the position information associated with each data point;

finding a subset of voxels in the plurality of voxels that include a point corresponding to a certain beam in the plurality of beams and an intensity value in a predetermined neighborhood of a certain intensity value;

estimating a probability density based on the subset of voxels; and

generating a calibration map that maps the certain intensity value of the certain beam to a calibrated intensity value for the certain beam, based on the probability density and a prior density.

14. A multi-beam Lidar intensity measurement calibration system comprising:

a positioning device which determines position information of a moving platform;

a plurality of beams;

a data acquisition unit which collects a plurality of data points, each data point including the position information and intensity information from the plurality of beams;

a processor configured to

segment the plurality of data points into a plurality of voxels, based on the position information associated with each data point,

find a subset of voxels in the plurality of voxels that include a point corresponding to a certain beam in the plurality of beams and an intensity value in a predetermined neighborhood of a certain intensity value;

estimate a probability density based on the subset of voxels; and

generate a calibration map that maps the certain intensity value of the certain beam to a calibrated intensity value for the certain beam, based on the probability density and a prior density.

15. The system of claim 14, wherein the positioning device includes one or more of a wheel odometry device, an inertial measurement device, or a global positioning system (GPS).