US 2026/0227199 A1 · ELEVATION MAP CONSTRUCTION METHOD AND SYSTEM FOR LEGGED ROBOT, AND STORAGE MEDIUM

Abstract

An elevation map construction method for motion planning of a legged robot includes: constructing a motion distortion removal model, a point cloud filtering model and an elevation map model, performing pose transformation on original measurement point data to obtain a current frame of point clouds with motion distortion removed, and then processing the current frame of point clouds to remove points unrelated to environmental information therein to obtain preprocessed point clouds; and constructing an elevation map for the motion planning of the legged robot by using the processed point clouds and the body odometer pose data of the legged robot. The solution is scientific, reasonable, and feasible, can effectively improve the accuracy of the elevation map, can facilitate promotion, and can achieve the accurate navigation of the robot. Moreover, the elevation map can be centered on the legged robot and move with the robot in real time.

Claims11

Dependent claims

An elevation map construction method for motion planning of a legged robot,

comprising the following steps:
step one:
acquiring original measurement point data of a map planning area;
step two:
performing pose transformation on the original measurement point data by using a preconstructed motion distortion removal model to obtain a current frame of point clouds with motion distortion removed;
step three:
performing processing on the current frame of point clouds through a preconstructed point cloud filtering model to remove points unrelated to environmental information therein to obtain preprocessed point clouds, wherein
the points unrelated to the environmental information comprise a body point cloud or/and a leg point cloud or/and a noise point cloud;
step four:
performing processing on the preprocessed point clouds by using a preconstructed elevation map model according to body odometer and/or visual odometer and/or laser odometer pose data to obtain a grid elevation value;
and
step five:
establishing or updating grids in the map planning area according to the grid elevation value to achieve elevation map construction for the motion planning of the legged robot.

10 more elements

An elevation map construction method for motion planning of a legged robot,

comprising the following steps:
S1:
acquiring a measurement point pz in a sensor coordinate system collected by a laser radar, and transforming the measurement point from the sensor coordinate system to a world coordinate system by using a body odometer pose estimate
(
R
b
w
,
t
b
w
)
and a pose transformation
(
R
s
b
+
t
s
b
)
obtained through calibration from a body coordinate system to the sensor coordinate system, wherein a world coordinate value of the measurement point is pw:
p
w
=
R
b
w
(
R
s
b
⁢
p
s
+
t
s
b
)
+
t
b
w
S2:
mapping the measurement point from the world coordinate system to a corresponding grid thereof in an elevation map, wherein a corresponding elevation value thereof is a z-axis component of a measurement point
p
w
=
(
p
x
w
,
p
y
w
,
p
z
w
)
,
i.e., an elevation measurement value of a current grid, and the elevation measurement value satisfies a Gaussian distribution
h
~
∼
N
⁡
(
μ
h
~
,
σ
h
~
2
)
;
and constructing a projection matrix MD=[0,0,1] to obtain a current elevation measurement mean value:
μ
h
~
=
M
p
⁢
p
w
=
M
p
[
R
b
w
(
R
s
b
⁢
p
s
+
t
s
b
)
+
t
b
w
]
computing a variance of a current elevation measurement value from covariance propagation:
σ
h
~
2
=
J
p
⁢
Σ
p
⁢
J
p
T
+
J
R
⁢
Σ
R
⁢
J
R
T
+
J
t
⁢
Σ
t
⁢
J
t
T
where Jv, JR, and Jt are Jacobian matrices of the elevation measurement value {tilde over (h)} with respect to ps,
R
b
w
,
and
⁢
t
b
w
;
Σv is a covariance matrix of the measurement point in the sensor coordinate system;
ΣR is a covariance matrix of a robot odometer rotation;
Σt is a covariance matrix of a robot odometer translation;
S3:
constructing a Gaussian distribution of the current elevation measurement value according to variances of the current elevation measurement mean value and the current elevation measurement value;
in a case that elevation estimates of grids satisfy the Gaussian distribution
h
~
∼
N
⁡
(
μ
h
~
,
σ
h
~
2
)
,
fusing the current elevation measurement into an elevation estimate of the current grid by computing a normalized product of two Gaussian distributions to obtain an updated elevation estimate:
μ
h
~
=
σ
h
~
2
⁢
μ
h
~
+
σ
h
~
2
⁢
μ
h
~
σ
h
~
2
+
σ
h
~
2
σ
h
~
2
=
σ
h
~
2
⁢
σ
h
~
2
σ
h
~
2
+
σ
h
~
2
S4:
constructing a dense elevation map for the motion planning of the legged robot according to the updated elevation estimate.

236 more elements

Dependencies are inferred from the claim text.

Elevation map construction method and system for legged robot, and storage medium

Life of this patent

Elevation map construction method and system for legged robot, and storage medium

Life of this patent

Abstract

Claims11