US 11,080,870 B2 · Method and apparatus for registering three-dimensional point clouds

Abstract

A method of registering three-dimensional (3D) point clouds may include obtaining a first 3D point cloud acquired at a first location; obtaining a second 3D point cloud acquired at a second location; calculating a first normal vector for each point of the first 3D point cloud to create a plurality of normal vectors; calculating, for each point of the first 3D point cloud, a normal deviation amount of the corresponding normal vector to other normal vectors in a predetermined neighborhood of the point; selecting, from the first 3D point cloud, a first registration region based on whether the normal deviation amount of each point meets a deviation threshold; and registering the first 3D point cloud and the second 3D point cloud to create the composite 3D point cloud, the registration utilizing the first registration region in place of the first 3D point cloud.

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Claims14

Dependent claims

A method of registering three-dimensional (3D) point clouds of an area to create a composite 3D point cloud of the area, the method comprising:

obtaining a first 3D point cloud comprising a plurality of first points, the first 3D point cloud being acquired at a first location;
obtaining a second 3D point cloud comprising a plurality of second points, the second 3D point cloud being acquired at a second location different from the first location;
calculating a first normal vector for each first point of the plurality of first points to create a plurality of first normal vectors;
for each first point of the plurality of first points, calculating a first normal vector deviation amount of a corresponding first normal vector compared to other first normal vectors in a predetermined neighborhood about the first point;
selecting, from the plurality of first points, a first registration region based on whether the first normal deviation amount of each first point of the first portion of points satisfies a predetermined condition;
and
registering the first 3D point cloud and the second 3D point cloud to create the composite 3D point cloud based on the first registration region;
wherein the first registration region comprises one or more of the plurality of first points having stable global coordinates over a period of time, the stable global coordinates defining a static object;
and
wherein the one or more of the plurality of first points having the stable global coordinates are configured to be automatically identified and included in the first registration region.

7 more elements

A method of registering three-dimensional (3D) point clouds of an area to create a composite 3D point cloud of the area, the method comprising

obtaining a first 3D point cloud comprising a plurality of first points, the first 3D point cloud being acquired at a first location;
obtaining a second 3D point cloud comprising a plurality of second points, the second 3D point cloud being acquired at a second location different from the first location;
calculating a first normal vector for each first point of the plurality of first points to create a plurality of first normal vectors;
for each first point of the plurality of first points, calculating a first normal deviation amount of a corresponding first vector compared to other first normal vectors in a predetermined neighborhood about the first point;
selecting, from the plurality of first points, a first registration region based on whether the first normal deviation amount of each first point of the plurality of first points satisfies a predetermined condition;
registering the first 3D point cloud and the second 3D point cloud to create the composite 3D point cloud based on the first registration region;
wherein the predetermined condition is a first deviation threshold;
and
wherein the selecting the first registration region comprises:
for each first point of the plurality of first points, comparing the first normal deviation amount of the first point to the first deviation threshold;
determining that the first normal deviation amount of a corresponding first point of the plurality of first points exceeds the first deviation threshold;
in response to the first normal deviation amount exceeding the first deviation threshold, adding the corresponding first point to a first exclusion set, the first exclusion set comprising corresponding first points of the plurality of first points which exceed the first deviation threshold, a first representation distinguishing the corresponding first points in the first exclusion set;
and
subtracting the first exclusion set from the first 3D point cloud to create the first registration region, a second representation distinguishing the first registration region, the second representation being different from the first representation.

11 more elements

A three-dimensional measuring system comprising:

a housing that is rotatable about a first axis;
a light source disposed within the housing and operable to emit light beams;
a beam steering mechanism coupled to the housing and configured to direct the emitted light beams onto an object surface within a scan area in the environment;
a light receiver disposed within the housing to receive light reflected from the object surface through the beam steering mechanism;
a processor system operably coupled to the light source, the beam steering mechanism, and the light receiver, the processor system being responsive to nontransitory executable computer instructions to determine 3D coordinates of a composite 3D point cloud of the object surface based at least in part on the emitting of the light beams and the receiving by the light receiver of the reflected light to define the composite 3D point cloud;
wherein the processor system is further responsive to nontransitory executable computer instructions to perform:
obtaining a first 3D point cloud comprising a plurality of first points, the first 3D point cloud being acquired at a first location;
obtaining a second 3D point cloud comprising a plurality of second points, the second 3D point cloud being acquired at a second location different from the first location;
calculating a first normal vector for each first point of the plurality of first points to create a plurality of first normal vectors;
for each first point of the plurality of first points, calculating a first normal deviation amount of a corresponding first normal vector compared to other first normal vectors in a predetermined neighborhood about the first point;
selecting, from the plurality of first points, a first registration region based on whether the first normal deviation amount of each first point of the plurality of first points satisfies a predetermined condition;
registering the first 3D point cloud and the second 3D point cloud to create the composite 3D point cloud based on the first registration region;
wherein the predetermined condition is a first deviation threshold;
and
wherein the selecting the first registration region comprises:
for each first point of the plurality of first points, comparing the first normal deviation amount of the first point to the first deviation threshold;
determining that the first normal deviation amount of a corresponding first point of the plurality of first points exceeds the first deviation threshold;
in response to the first normal deviation amount exceeding the first deviation threshold, adding the corresponding first point to a first exclusion set, the first exclusion set comprising corresponding first points of the plurality of first points which exceed the first deviation threshold, a first representation distinguishing the corresponding first points in the first exclusion set;
and
subtracting the first exclusion set from the first 3D point cloud to create the first registration region, a second representation distinguishing the first registration region, the second representation being different from the first representation.

17 more elements

A non-transitory computer-readable medium storing thereon computer-executable instructions that, when executed by a computer, cause the computer to perform:

obtaining a first 3D point cloud comprising a plurality of first points, the first 3D point cloud being acquired at a first location;
obtaining a second 3D point cloud comprising a plurality of second points, the second 3D point cloud being acquired at a second location different from the first location;
calculating a first normal vector for each first point of the plurality of first points to create a plurality of first normal vectors;
for each first point of the plurality of first points, calculating a first normal vector deviation amount of the corresponding first normal vector compared to and other first normal vector in a predetermined neighborhood about the first point;
selecting, from the plurality of first points, a first registration region based on whether the first normal deviation amount of each first point of the plurality of first points satisfies a predetermined condition;
and
registering the first 3D point cloud and the second 3D point cloud to create a composite 3D point cloud based on the first registration region;
wherein the predetermined condition is a first deviation threshold;
and
wherein the selecting the first registration region comprises:
for each first point of the plurality of first points, comparing the first normal deviation amount of the first point to the first deviation threshold;
determining that the first normal deviation amount of a corresponding first point of the plurality of first points exceeds the first deviation threshold;
in response to the first normal deviation amount exceeding the first deviation threshold, adding the corresponding first point to a first exclusion set, the first exclusion set comprising corresponding first points of the plurality of first points which exceed the first deviation threshold, a first representation distinguishing the corresponding first points in the first exclusion set;
and
subtracting the first exclusion set from the first 3D point cloud to create the first registration region, a second representation distinguishing the first registration region, the second representation being different from the first representation.

12 more elements

Dependencies are inferred from the claim text.

Description7,418 words

Summary1,235 words

#The subject matter disclosed herein relates to use of a 3D laser scanner time-of-flight (TOF) coordinate measurement device. A 3D laser scanner of this type steers a beam of light to a non-cooperative target such as a diffusely scattering surface of an object. A distance meter in the device measures a distance to the object, and angular encoders measure the angles of rotation of two axles in the device. The measured distance and two angles enable a processor in the device to determine the 3D coordinates of the target.

#A TOF laser scanner is a scanner in which the distance to a target point is determined based on the speed of light in air between the scanner and a target point. Laser scanners are typically used for scanning closed or open spaces such as interior areas of buildings, industrial installations and tunnels. They may be used, for example, in industrial applications and accident reconstruction applications. A laser scanner optically scans and measures objects in a volume around the scanner through the acquisition of data points representing object surfaces within the volume. Such data points are obtained by transmitting a beam of light onto the objects and collecting the reflected or scattered light to determine the distance, two-angles (i.e., an azimuth and a zenith angle), and optionally a gray-scale value. This raw scan data is collected, stored and sent to a processor or processors to generate a 3D image representing the scanned area or object.

#Generating an image requires at least three values for each data point. These three values may include the distance and two angles, or may be transformed values, such as the x, y, z coordinates. In an embodiment, an image is also based on a fourth gray-scale value, which is a value related to irradiance of scattered light returning to the scanner.

Brief description of the drawings13 figures

#The subject matter, which is regarded as the invention, is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

#FIG. 1 is a perspective view of a laser scanner in accordance with an embodiment;

#FIG. 2 is a side view of the laser scanner illustrating a method of measurement according to an embodiment;

Detailed description5,893 words

#The detailed description explains embodiments of the invention, together with advantages and features, by way of example with reference to the drawings.

#At least an embodiment may relate to a 3D measuring device having a 3D scanner and at least one photographic camera. Embodiments provide advantages acquiring three-dimensional (3D) coordinates of an area of the environment that is shadowed from the 3D camera using a photogrammetry camera. Embodiments of the invention provide advantages in acquiring 3D coordinates of a floor area below the 3D scanner using a photogrammetry camera.

#Referring now to FIGS. 1-3, a laser scanner 20 is shown for optically scanning and measuring the environment surrounding the laser scanner 20. The laser scanner 20 has a measuring head 22 and a base 24. The measuring head 22 is mounted on the base 24 such that the laser scanner 20 may be rotated about a vertical axis 23. In one embodiment, the measuring head 22 includes a gimbal point 27 that is a center of rotation about the vertical axis 23 and a horizontal axis 25. The measuring head 22 has a rotary mirror 26, which may be rotated about the horizontal axis 25. The rotation about the vertical axis may be about the center of the base 24. The terms vertical axis and horizontal axis refer to the scanner in its normal upright position. It is possible to operate a 3D coordinate measurement device on its side or upside down, and so to avoid confusion, the terms azimuth axis and zenith axis may be substituted for the terms vertical axis and horizontal axis, respectively. The term pan axis or standing axis may also be used as an alternative to vertical axis.

Facts

Application

16/445,786Filed 19 Jun 2019

Published

US 2020/0402244 A124 Dec 2020

Granted

3 Aug 2021

Term ends

19 Jun 2039

Estimated · how

20 years from earliest known nonprovisional domestic filing plus recorded patent term adjustment. Maintenance-fee lapses, terminal disclaimers and statutory extensions have not been checked. The continuity chain may be incomplete; this is not an enforceability determination.

Classification13 codes

G01S 7/4808
Of systems according to group G01S17/00: evaluating distance, position or velocity data
G06T 7/33
Determination of transform parameters for the alignment of images, i.e. image registration: using feature-based methods
G01C 11/14
The pictures being supported in the same relative position as when they were taken: with optical projection
G01S 17/86
Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems: combinations of lidar systems…

Method and apparatus for registering three-dimensional point clouds

Life of this patent

Method and apparatus for registering three-dimensional point clouds

Life of this patent

Life of this patent

Abstract

Drawings

Drawings

Claims14

Description7,418 words

Drawings

Classification13 codes

Life of this patent

Description7,418 words