US 11,448,765 B2Granted

Adaptive filtering method of photon counting lidar for bathymetry

AssigneeChina University of Geosciences

InventorsLizhe Wang, Yifu Chen, Yuan Le, Gang Chen, Weitao Chen

Life of this patent

Estimating the term…

Abstract

An adaptive filtering method of photon counting Lidar for bathymetry is provided in this invention, which includes steps: step S1: adaptively acquiring parameters of elliptic filtering for water surface photon signals; step S2: determining a relationship between filter parameters and elevation of underwater photon signals, and obtaining parameters of the elliptic filtering for photon signal in water column; and step S3: filtering and fitting the water surface photon signals and the underwater photon signals to acquire continuous bathymetry results.

Claims3

Dependent claims

An adaptive filtering method of photon counting Lidar for bathymetry, applied to laser detection technology, comprising steps of:

step S1:
adaptively acquiring parameters of elliptic filtering for water surface photon signals;
wherein in the step S1, the step of adaptively acquiring parameters of elliptic filtering for water surface photon signals comprises:
sub-step S11:
acquiring raw point cloud data P of the photon counting Lidar:
P=(Di, Hi),i∈[1,Np],
wherein Di is an along-track distance of each photon signal, Hi is an elevation of each photon signal, and Np is a number of photon included in the raw point cloud data;
sub-step S12:
with Δh as a slice interval of an elevation direction, cutting original data into m slices in the elevation direction:
m
=
max
⁡
(
H
i
)
-
min
⁡
(
H
i
)
Δ
⁢
h
;
sub-step S13:
for m slices sliced in the sub-step S12, counting the number xm of photon signals in each slice:
h
m
=
min
⁡
(
H
i
)
+
2
⁢
m
-
1
2
⁢
Δ
⁢
h
,
wherein hm is a center elevation of each elevation slice, and xm is a number of photon signals in the slices;
sub-step S14:
fitting a relationship between the center elevation of the elevation slice hm and the number of photon signals in the slices xm with a normal distribution curve to obtain a fitting relationship f(x):
f
⁡
(
x
)
=
x
maxpho
⁢
exp
⁡
(
-
(
x
-
h
maxpho
)
2
2
⁢
σ
2
)
,
wherein xmaxpho indicates a number of photon signals in the elevation slice containing the most photon signals, hmaxpho indicates the elevation of this slice, σ is a standard deviation of a normal distribution, and x indicates a normally-distributed independent variable;
sub-step S15:
with hmaxpho in the step S4 as a center and twice the normal distribution standard deviation as a left and right threshold, determining an elevation boundary line of the water surface photons while determining to search a semi-minor axis b of the ellipse for filtering:
{
H
min
′
=
h
maxpho
-
2
⁢
σ
H
max
′
=
h
maxpho
+
2
⁢
σ
,
b
=
2
⁢
σ
,
wherein H′min is an elevation lower limit of the water surface photons, and H′max is an elevation upper limit of the water surface photons;
sub-step S16:
with the elevation lower limit H′min and the elevation upper limit H′max of the water surface photons as boundaries, acquiring the elevation of each water surface photon to form a water surface photon data set SP and an underwater photon data set WP:
{
S
⁢
P
=
(
S
⁢
D
k
,
S
⁢
H
k
)
,
k
∈
[
1
,
N
s
⁢
p
]
W
⁢
P
=
(
W
⁢
D
l
,
W
⁢
H
l
)
,
l
∈
[
1
,
N
w
⁢
p
]
,
wherein SDk indicates the along-track distance of a water surface photon in the data set, SHk indicates the elevation of the photon on water surface, and Nsp is a number of photons in the water surface data set that meet the following conditions:
H′min<SHk<H′max,
WDl indicates an along-track distance of a underwater photon in the data set, WHl indicates an elevation of the underwater photon, and Nwp is the number of photons in the underwater data set that meet the following conditions:
WHl<H′min;
sub-step S17:
in the water surface photon data set SP, with a length of an along-track resolution Δd of the photon counting Lidar as the boundary, placing the water surface photons into q data sets WPu:
q
=
max
⁡
(
S
⁢
D
k
)
-
min
⁡
(
S
⁢
D
k
)
Δ
⁢
d
,
W
⁢
P
u
=
(
S
⁢
D
u
,
S
⁢
H
u
)
,
u
∈
[
1
,
q
]
;
sub-step S18:
counting a maximum difference of elevation for photons in the q sets in the elevation direction ΔSHu, and acquiring a mean Ratio_ab, thereby acquiring a semi-major axis a:
Δ
⁢
S
⁢
H
u
=
max
⁡
(
S
⁢
H
u
)
-
min
⁡
(
S
⁢
H
u
)
,
Ratio_ab
=
∑
q
u
=
1
Δ
⁢
d
/
Δ
⁢
SH
u
q
,
a
=
b
*
Ratio_ab
,
wherein Ratio_ab is a ratio of the major axis to the minor axis of the search ellipse;
sub-step S19:
in the water surface photon data set SP, traversing all data points, and then constructing a search region with the point as a center, a as the major axis and b as the minor axis of the ellipse to count the number of the points pv, v=1, 2, 3 . . . Nsp in the search region, and a mean of the number of the points is an initial minimum number of the points minpts:
minpts
=
∑
v
=
1
N
s
⁢
p
p
v
N
s
⁢
p
;
sub-step S110:
traversing each point in the water surface photon data set SP, and with the point (SDk, SHk) as a center and a, b as the semi-major axis and the semi-minor axis of the elliptical search region respectively, determining whether the remaining points (SDx, SHx) in the water surface photon data set SP are located in the search region:
d
⁢
i
⁢
s
⁢
s
x
=
(
S
⁢
D
x
-
S
⁢
D
k
)
2
a
+
(
S
⁢
H
x
-
S
⁢
H
k
)
2
b
,
if dissx<1, the point (SDx, SHx) is in the elliptical search region with (SDk, SHk) as a center, and then counting the number of all the signal points in the search region;
if this value is larger than minpts, then marking (SDk, SHk) as a signal point, otherwise being a noise point;
step S2:
determining a relationship between filter parameters and elevation of underwater photon signals, and obtaining parameters of the elliptic filtering for photon signal in water column;
wherein in the step S2, the step of determining a relationship between filter parameters and elevation of underwater photon signals comprises:
sub-step S21:
determining how the underwater photons change with a water depth after filtering the water surface photons, basing on the data set (hm, xm) formed in sub-step S13, a relationship coefficient af is obtained;
sub-step S22:
in the underwater photon data set WP, with the point (WDl, WHl) as a center, the semi-major axis al and the semi-minor axis bl and a minimum point number threshold minptsl of the elliptical search region being respectively related to a major axis, a minor axis, the minimum point number threshold initial value, and the relationship coefficient:
{
a
l
=
b
l
×
Ratio_ab
b
l
=
b
×
(
1
+
a
f
×
abs
(
WH
l
-
H
min
′
)
)
,
m
⁢
inpts
l
=
minp
⁢
t
⁢
s
×
(
1
-
a
f
×
abs
(
WH
l
-
H
min
′
)
)
,
l
∈
[
1
,
N
w
⁢
p
]
,
rotating the search ellipse counterclockwise at intervals of Δθ to form kθ elliptical search regions with different directions:
kθ=2π/Δθ;
and
step S3:
filtering and fitting the water surface photon signals and the underwater photon signals to acquire continuous bathymetry results;
wherein in the step S3, the step of acquiring bathymetry results comprises:
sub-step S31:
traversing each point in the underwater photon data set WP, and determining whether the remaining points (WDx, WHx) in the underwater photon data set WP are located in an w elliptical search region with the point (WDl, WHl) as a center and al, bl as the semi-major axis and the semi-minor axis;
d
⁢
i
⁢
s
⁢
w
w
=
x
w
2
a
l
+
y
w
2
b
l
,
w
=
1
,
2
,
3
,
…
⁢
k
θ
,
wherein:
xw=(WDx−WDk)×sin(w×Δθ)+(WHx−WHk)×cos(w×Δθ)
yw=(WDx−WDk)×sin(w×Δθ)+(WHx−WHk)×cos(w×Δθ),
if diswx<1, indicating that the point (WDx, WHx) is in a rotated elliptical search region with (WDl, WHl) as a center, and the number of all signal points in the rotated elliptical search region is counting as minptsw;
selecting an ellipse including the largest number minptsw of photon points as a filter ellipse for the point in all rotated ellipses;
minptswl=max(minptsw),w=1,2,3, . . . kθ,
if minptswl is larger than the minimum number minptsl of the points, then marking (WDl, WHl) as the signal point, otherwise being a noise point;
wherein the adaptive filtering method of photon counting Lidar for bathymetry is applied to automatically and efficiently extract the water surface and underwater photon signals and to acquire continuous water depth data by performing the adaptive filtering in combination with gathering data using the LIDAR.

456 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/134,561Filed 28 Dec 2020
Priority: CN 202010081826.06 Feb 2020
Published: US 2021/0116570 A122 Apr 2021
Granted: 20 Sep 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G01S 17/89
Lidar systems specially adapted for specific applications: for mapping or imaging
G01C 13/008
Surveying specially adapted to open water, e.g. sea, lake, river or canal: measuring depth of open water
G01S 7/4813
Common to transmitter and receiver: housing arrangements
G01S 7/4861
Receivers: circuits for detection, sampling, integration or read-out

Family

US 2021/0116570 A1Pre-grant publication · published 22 Apr 2021
US 11,448,765 B2This patent

From the US filing history. Foreign family members aren’t listed.

Timeline

Citations

Open elsewhere

patentgrep

Loading patent…

Abstract

An adaptive filtering method of photon counting Lidar for bathymetry, applied to laser detection technology, comprising steps of:

step S1:
adaptively acquiring parameters of elliptic filtering for water surface photon signals;
wherein in the step S1, the step of adaptively acquiring parameters of elliptic filtering for water surface photon signals comprises:
sub-step S11:
acquiring raw point cloud data P of the photon counting Lidar:
P=(Di, Hi),i∈[1,Np],
wherein Di is an along-track distance of each photon signal, Hi is an elevation of each photon signal, and Np is a number of photon included in the raw point cloud data;
sub-step S12:
with Δh as a slice interval of an elevation direction, cutting original data into m slices in the elevation direction:
m
=
max
⁡
(
H
i
)
-
min
⁡
(
H
i
)
Δ
⁢
h
;
sub-step S13:
for m slices sliced in the sub-step S12, counting the number xm of photon signals in each slice:
h
m
=
min
⁡
(
H
i
)
+
2
⁢
m
-
1
2
⁢
Δ
⁢
h
,
wherein hm is a center elevation of each elevation slice, and xm is a number of photon signals in the slices;
sub-step S14:
fitting a relationship between the center elevation of the elevation slice hm and the number of photon signals in the slices xm with a normal distribution curve to obtain a fitting relationship f(x):
f
⁡
(
x
)
=
x
maxpho
⁢
exp
⁡
(
-
(
x
-
h
maxpho
)
2
2
⁢
σ
2
)
,
wherein xmaxpho indicates a number of photon signals in the elevation slice containing the most photon signals, hmaxpho indicates the elevation of this slice, σ is a standard deviation of a normal distribution, and x indicates a normally-distributed independent variable;
sub-step S15:
with hmaxpho in the step S4 as a center and twice the normal distribution standard deviation as a left and right threshold, determining an elevation boundary line of the water surface photons while determining to search a semi-minor axis b of the ellipse for filtering:
{
H
min
′
=
h
maxpho
-
2
⁢
σ
H
max
′
=
h
maxpho
+
2
⁢
σ
,
b
=
2
⁢
σ
,
wherein H′min is an elevation lower limit of the water surface photons, and H′max is an elevation upper limit of the water surface photons;
sub-step S16:
with the elevation lower limit H′min and the elevation upper limit H′max of the water surface photons as boundaries, acquiring the elevation of each water surface photon to form a water surface photon data set SP and an underwater photon data set WP:
{
S
⁢
P
=
(
S
⁢
D
k
,
S
⁢
H
k
)
,
k
∈
[
1
,
N
s
⁢
p
]
W
⁢
P
=
(
W
⁢
D
l
,
W
⁢
H
l
)
,
l
∈
[
1
,
N
w
⁢
p
]
,
wherein SDk indicates the along-track distance of a water surface photon in the data set, SHk indicates the elevation of the photon on water surface, and Nsp is a number of photons in the water surface data set that meet the following conditions:
H′min<SHk<H′max,
WDl indicates an along-track distance of a underwater photon in the data set, WHl indicates an elevation of the underwater photon, and Nwp is the number of photons in the underwater data set that meet the following conditions:
WHl<H′min;
sub-step S17:
in the water surface photon data set SP, with a length of an along-track resolution Δd of the photon counting Lidar as the boundary, placing the water surface photons into q data sets WPu:
q
=
max
⁡
(
S
⁢
D
k
)
-
min
⁡
(
S
⁢
D
k
)
Δ
⁢
d
,
W
⁢
P
u
=
(
S
⁢
D
u
,
S
⁢
H
u
)
,
u
∈
[
1
,
q
]
;
sub-step S18:
counting a maximum difference of elevation for photons in the q sets in the elevation direction ΔSHu, and acquiring a mean Ratio_ab, thereby acquiring a semi-major axis a:
Δ
⁢
S
⁢
H
u
=
max
⁡
(
S
⁢
H
u
)
-
min
⁡
(
S
⁢
H
u
)
,
Ratio_ab
=
∑
q
u
=
1
Δ
⁢
d
/
Δ
⁢
SH
u
q
,
a
=
b
*
Ratio_ab
,
wherein Ratio_ab is a ratio of the major axis to the minor axis of the search ellipse;
sub-step S19:
in the water surface photon data set SP, traversing all data points, and then constructing a search region with the point as a center, a as the major axis and b as the minor axis of the ellipse to count the number of the points pv, v=1, 2, 3 . . . Nsp in the search region, and a mean of the number of the points is an initial minimum number of the points minpts:
minpts
=
∑
v
=
1
N
s
⁢
p
p
v
N
s
⁢
p
;
sub-step S110:
traversing each point in the water surface photon data set SP, and with the point (SDk, SHk) as a center and a, b as the semi-major axis and the semi-minor axis of the elliptical search region respectively, determining whether the remaining points (SDx, SHx) in the water surface photon data set SP are located in the search region:
d
⁢
i
⁢
s
⁢
s
x
=
(
S
⁢
D
x
-
S
⁢
D
k
)
2
a
+
(
S
⁢
H
x
-
S
⁢
H
k
)
2
b
,
if dissx<1, the point (SDx, SHx) is in the elliptical search region with (SDk, SHk) as a center, and then counting the number of all the signal points in the search region;
if this value is larger than minpts, then marking (SDk, SHk) as a signal point, otherwise being a noise point;
step S2:
determining a relationship between filter parameters and elevation of underwater photon signals, and obtaining parameters of the elliptic filtering for photon signal in water column;
wherein in the step S2, the step of determining a relationship between filter parameters and elevation of underwater photon signals comprises:
sub-step S21:
determining how the underwater photons change with a water depth after filtering the water surface photons, basing on the data set (hm, xm) formed in sub-step S13, a relationship coefficient af is obtained;
sub-step S22:
in the underwater photon data set WP, with the point (WDl, WHl) as a center, the semi-major axis al and the semi-minor axis bl and a minimum point number threshold minptsl of the elliptical search region being respectively related to a major axis, a minor axis, the minimum point number threshold initial value, and the relationship coefficient:
{
a
l
=
b
l
×
Ratio_ab
b
l
=
b
×
(
1
+
a
f
×
abs
(
WH
l
-
H
min
′
)
)
,
m
⁢
inpts
l
=
minp
⁢
t
⁢
s
×
(
1
-
a
f
×
abs
(
WH
l
-
H
min
′
)
)
,
l
∈
[
1
,
N
w
⁢
p
]
,
rotating the search ellipse counterclockwise at intervals of Δθ to form kθ elliptical search regions with different directions:
kθ=2π/Δθ;
and
step S3:
filtering and fitting the water surface photon signals and the underwater photon signals to acquire continuous bathymetry results;
wherein in the step S3, the step of acquiring bathymetry results comprises:
sub-step S31:
traversing each point in the underwater photon data set WP, and determining whether the remaining points (WDx, WHx) in the underwater photon data set WP are located in an w elliptical search region with the point (WDl, WHl) as a center and al, bl as the semi-major axis and the semi-minor axis;
d
⁢
i
⁢
s
⁢
w
w
=
x
w
2
a
l
+
y
w
2
b
l
,
w
=
1
,
2
,
3
,
…
⁢
k
θ
,
wherein:
xw=(WDx−WDk)×sin(w×Δθ)+(WHx−WHk)×cos(w×Δθ)
yw=(WDx−WDk)×sin(w×Δθ)+(WHx−WHk)×cos(w×Δθ),
if diswx<1, indicating that the point (WDx, WHx) is in a rotated elliptical search region with (WDl, WHl) as a center, and the number of all signal points in the rotated elliptical search region is counting as minptsw;
selecting an ellipse including the largest number minptsw of photon points as a filter ellipse for the point in all rotated ellipses;
minptswl=max(minptsw),w=1,2,3, . . . kθ,
if minptswl is larger than the minimum number minptsl of the points, then marking (WDl, WHl) as the signal point, otherwise being a noise point;
wherein the adaptive filtering method of photon counting Lidar for bathymetry is applied to automatically and efficiently extract the water surface and underwater photon signals and to acquire continuous water depth data by performing the adaptive filtering in combination with gathering data using the LIDAR.

456 more elements

Facts

Application

17/134,561Filed 28 Dec 2020

Priority

CN 202010081826.06 Feb 2020

Published

US 2021/0116570 A122 Apr 2021

Granted

20 Sep 2022

Term ends

Not estimatedThe source records don’t support an estimate.

Classification7 codes

G01S 17/89
Lidar systems specially adapted for specific applications: for mapping or imaging
G01C 13/008
Surveying specially adapted to open water, e.g. sea, lake, river or canal: measuring depth of open water
G01S 7/4813
Common to transmitter and receiver: housing arrangements
G01S 7/4861
Receivers: circuits for detection, sampling, integration or read-out