Sensors 2018, 18, 825 4 of 18
with a 2D laser range finder carried by the robot and segment the ranging data into clusters. After that,
we find the neighboring clusters (two neighbors) in the continuous time and treat the two clusters
as the same obstacle, which is used to estimate the distance-based velocity. This part of information
acquisition and data processing is used for subsequent velocity matching and the particle filtering.
In the other part, we first calculate the similarity between phase-based velocity and the
distance-based velocity and then determine the velocity matching according to a defined similarity
rule. In addition, we choose the best K clusters with the best velocity similarity score to facilitate the
processing of the particle filtering. To improve localization accuracy and robustness of the system,
we use different prediction methods for the particle filter based on the velocity matching results:
(1) If the
velocity matching is successful, we can find the effective laser cluster for prediction, which is
referred to as laser prediction in this paper. (2) If the velocity matching is unsuccessful, we choose
another method called the random prediction method. After that, we update the particle’s weight in the
particle filter using the best K clusters. Finally, we localize the moving object by iteratively performing
prediction, update, and resampling of the particle filter. The role of this part is to improve the
positioning efficiency and accuracy by complementing the RFID phase with laser ranging information
using a particle filter.
RFID system
Estimate the RFID
phase-based velocity
2D laser
range finder
Laser
clustering process
RFID
phase information
Estimate the velocity
and moving direction
of the cluster
Velocity matching
K Clusters with the
highest similiarity
Particle filter
update
Estimate pose
Particle filter
initialization
Particle filter
prediction
Random
prediction
Laser
prediction
match mismatch
First part: Information collection and processing
Second part: Sensor Fusion with a particle filter
Figure 1. System overview.
3. Moving Object Localization Based on the Particle Filtering
We describe the details of the system in this section. In particular, the computation of RFID phase
velocity is described in Section 3.1, the clustering of laser ranging data is presented in Section 3.2,
the estimation of the velocity and moving direction of a cluster is detailed in Section 3.3, and
velocity matching and the implementation using a particle filter are detailed in Sections 3.4 and 3.5,
respectively. The mathematical symbols and their meanings used in this paper are listed in Table 1.
3.1. Computing RFID Phase-Based Velocity
The phase obtained by RFID is a periodical function, which can be described as:
ϕ
t
=
2π · d
t
λ
· mod(2π), (1)
where
ϕ
t
is the signal phase at time
t
,
λ
is the wavelength of the receiving signal, and
d
t
is the distance
from RFID tag to the antenna. In practice, signal phase is influenced by the transmitter, receiver,
and tag’s
reflection characteristics [
30
], which introduce additional phase rotation. Therefore, the
phase of RFID signal could be modeled as: