# US 2014/0152971 A1 · Method and apparatus for intensity calibration of multi-beam lidar sensors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0152971 A1 - Document ID: US20140152971A1 - Status: Published application - Kind: A1 - Application number: 13/705,212 - Filed: 2012-12-05 - Published: 2014-06-05 - Earliest claimed: 2012-12-05 - Assignees: Toyota Motor Engineering & Manufacturing North America (TEMA) - Applicants: Toyota Motor Engineering & Manufacturing North - Inventors: Michael R. JAMES - Web page: https://patentgrep.com/patent/US20140152971A1 ## CPC - G01S 17/42: Simultaneous measurement of distance and other co-ordinates (indirect measurement G01S17/46) - G01S 7/497: Means for monitoring or calibrating - G01S 17/89: for mapping or imaging - G01C 15/002: {Active optical surveying means (optical plumbing G01C15/105)} - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) ## 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 ### Claim 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 2) 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. ### Claim 4 (depends on 1) The method of claim 1, wherein the prior density function is a Gaussian density function. ### Claim 5 (depends on 1) The method of claim 1, wherein the calibration map is a maximum likelihood calibration map. ### Claim 6 (depends on 1) 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). ### Claim 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. ### Claim 8 (depends on 7) 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. ### Claim 9 (depends on 8) 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. ### Claim 10 (depends on 7) The apparatus of claim 7, wherein the processor uses a Gaussian density function as the prior density function. ### Claim 11 (depends on 7) The apparatus of claim 7, wherein the processor generates a maximum likelihood calibration map as the calibration map. ### Claim 12 (depends on 7) 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). ### Claim 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. ### Claim 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. ### Claim 15 (depends on 14) 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).