Systems, Methods and Devices for Map-Based Object's Localization Deep Learning and Object's Motion Trajectories on Geospatial Maps Using Neural Network
An object of initial unknown position on a map may be determined by traversing through moving and turning to establish motion trajectory to reduce its spatial uncertainty to a single location that would fit only to a certain map trajectory. A artificial neural network model learns from object motion on different map topologies may establish the object's end-to-end positioning from embedding map topologies and object motion. The proposed method includes learning potential motion patterns from the map and perform trajectory classification in the map's edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and both a Graph Neural Network and an RNN are trained from the map for each representation to compare their performances. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize the object based on its motion trajectories.
1 - 10 . (canceled)
11 . A method for determining object motion trajectories, comprising:
in response to receiving motion based sequence of discrete distances {l 1 , l 2 . . . l n−1 } and directions {φ1 . . . φn−2} of object's trajectories at time t i generated from at least one device of an object that traverses within a map M,
executing by a processor, an algorithm stored in a memory of the at least one device of the object to perform steps, comprising:
determining a geolocation probability P loc of an object according to the sequence of discrete distances {l 1 , l 2 . . . l n−1 } and directions {φ1 . . . φn−2} of object's trajectories at the time t, wherein the geolocation probability P loc of the object's motion trajectories as equation (1):
P
l
o
c
=
P
(
s
t
|
ϕ
,
β
1
:
t
,
M
)
(
1
)
where, s t is an output edge id, and the P loc indicates an output result conditioned on the topological map M, and the sequence of direction with a turning angle φi at a node v i and a distance β between nodes, where t is time sequence.
12 . The method of claim 11 , comprising:
training a recurrent neural network (RNN) to determine the object motions output y t over the time sequence t based on hidden states h s expressed as equation (2):
h
s
=
f
α
(
x
s
,
h
s
-
1
)
(
2
)
y
t
=
f
β
(
h
t
)
where ƒ β is a linear function, ƒ α is a non-linear function, x t is a current input, h t−1 is a previous hidden state.
13 . The method of claim 12 , comprising:
using equation (2) to calculate an edge probability of each output Y with an edge id i at the time sequence t according to a softmax function as equation (3):
P
(
Y
=
i
|
y
)
=
softmax
(
y
)
=
e
y
Σ
j
=
0
k
e
y
(
3
)
14 . The method of claim 13 , comprising training the RNN using a negative log likelihood loss (NLL) on the edge probability in equation (3) based on equation (4):
L
i
=
-
log
(
p
y
i
)
(
4
)
wherein L i is a loss likelihood.
15 . The method of claim 14 , comprising determining temporal inconsistencies in the geolocation probability of the object's motion trajectories due to loss likelihood, by determining a conditional probability of hypothesis at the time sequence t using equation (5):
P
(
H
t
ij
❘
H
t
-
1
i
)
=
P
(
H
t
ij
,
H
t
-
1
i
)
P
(
H
t
-
1
i
)
(
5
)
wherein i=1, . . . , n 1 , j=1, . . . , n 2 and n 1 , n 2 are number of hypotheses in previous time sequence t−1 and current time sequence t.
16 . The method of claim 15 , comprising generating object's motion trajectories utilizing visual, inertial or visual-inertial odometry with six degrees of freedom including three-dimensional (3D) position and orientation using image data from a camera by detecting and matching features between consecutive frames, wherein the image data for features matching comprising relative rotation R and translation .
17 . The method of claim 16 , comprising computing an accuracy of geolocation as a function of length of trajectories segment, using equation (6):
Accuracy
(
i
)
=
1
N
∑
j
=
0
N
T
ji
(
6
)
T ji is correctness of prediction, {0, 1}, on i-th node of trajectories j and N is a total number of training trajectories used to generate a plot.