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TP3: Simultaneous Localization

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TP3: Simultaneous Localization and
Mapping using Extended Kalman Filter
Navigation pour les syst`emes autonomes
ROB312

1 Introduction
In this practical work, we will study a Simultaneous Localization and Mapping (SLAM) method that builds a map of an unknown environment using an
Extended Kalman Filter (EKF).
1.1 Mapping with backtracking
The goal of mapping with backtracking is propagating new information on
current pose to the previous poses. In order to achieve this goal:
• memorize relations between robot poses and map elements to be able to
correct them
• Possible with scan maps or landmarks maps
The general framework is shown as (1.1) (Bayesian filtering on robot pose
and map elements Possible with scan maps or landmarks maps).
Bel(xt, ct) = ηp(yt|xt, ct)
Z Z p(xt|xt−1, ut−1))Bel(xt−1, ct−1)dxt−1dct−1
(1.1)
Generally, there are many ways to approximate and estimate Bel (x, c),
we will study two in depth: the extended Kalman filter (this TP) and the
particulate filter (the next TP).
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1.2 Principle of EKF SLAM
For each landmark that is sensed, if it is already in the map, the Kalman
filter will be used to estimate its pose and the robot’s pose. The formula is
shown as (1.2).
x

t = f(Xt−1, ut)
P

t = A.Pˆ
t−1.AT + B.Q.BT
Y

t = h(x

t
)
K = P

t HT
.(H.P∗
t
.HT + PY )
−1

t = x

t + K(Yt − Y

t
)

t = P

t − KHP∗
t
(1.2)
If it is not in the map, it will be added to the state vector. If necessary, X
with absolute iconic pose and P with correct co-variance will be expanded by
calculating Jr and Jy.
1.3 Introduction of TP
The provided code simulates the robot moving along a given trajectory in
an environment composed of punctual landmarks. A simple extended Kalman
filtering method using the direction and distance perception of these landmarks
is realized, and the result is shown in Figure 1.1.
In the left figure, the red curve is the estimated position of EKF, the green
curve is the odometer trajectory, and the black curve is the real trajectory. In
the figure on the right, the blue line is the error between the estimated position
and the true position, and the red line is the co-variance.
Figure 1: Result of the original condition
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2 Influence of the environment
For this question, use the default parameters of the provided code. By default, the data association is assumed to be known, i.e. for each perceived
landmark, the corresponding landmark in the map is identified without ambiguities. In particular, this makes it possible to properly manage loop closures,
even when the error in the map is very severe.
In this section, we will firstly analyze the influence of the environment on
the performance of the algorithm. We will analyze the number of positions of
landmarks and the robot trajectory in question 1 and the Mahalanobis distance
is used in question 2.
2.1 Question 1 : Modify the number and position of landmarks and the robot trajectory
We use different numbers and positions of landmarks and robot trajectories
in the following three cases. Among them, the parameter yaw rate is related
to the length of the loop, and Landmarks is the definition of landmarks. It
should be noted that landmarks that are too close may cause some problems,
so landmarks should be selected reasonably.
2.1.1 Case 1 : a short loop and a dense map with many landmarks
inside the robot perception radius
In this case, we choose a larger yaw rate (0.2) to obtain a shorter cycle and
we create more landmarks (10) to increase the density of the map.
Figure 2: Result of the case 1
Figure 2 shows a high-quality map. The black curve (true trajectory) and the
red curve (estimated curve) overlap very well, and the location of each landmark
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is well located. However, it can be found that landmarks close to the starting
position are better positioned than landmarks far from the starting position,
because the corresponding uncertainty is smaller. The figure on the right also
shows this, where errors accumulate during the cycle and are corrected at the
starting point, therefore uncertainty.
In short loops and dense maps, the robot can keep many landmarks within
its perception radius during movement. Through data association and matrix
update, a good map can be obtained.
2.1.2 Case 2 : a long loop and a dense map with many landmarks
all along the loop
In this case, set the value of the yaw rate to 0.1 to get a longer loop, and
add landmarks along the loop.
Figure 3: Result of the case 2
We can see from the Figure 3 that the quality of the map is also very good,
and the red curve (estimated position) and black curve (real position) overlap
very well. The location of each landmark is also well positioned. It can still
be found that landmarks near the starting position are better positioned than
landmarks far from the starting position. In the figure on the right, errors and
uncertainties also accumulate during the cycle and are corrected at the starting
point.
Large-scale perception (10) allows the robot to retain many landmarks within
its perception radius during movement, which ultimately provides us with a good
map.
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2.1.3 Case 3 : a long loop and a sparse map with only few landmarks
near the start position
In this case, there are only a few landmarks near the starting location.
In Figure 4, we can see that although the landmarks in the map have been
located, because they are close to the starting position, the three curves no
longer overlap. The larger error and larger uncertainty in the figure on the
right also indicate this. The lack of landmarks makes it impossible for the robot
to correct and update its predictions, which leads to this phenomenon. Until
the starting position is approached again, the estimated curve can be corrected
again.
Figure 4: Result of the case 3
2.2 Question 2 : Answer the same question when the data
association is performed using the Mahalanobis distance
In this section, we study the case when the data association is performed using the Mahalanobis distance (KNOW N DAT A ASSOCIAT ION = 0). The
parameter M DIST T H should be modified according to the environment.
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Figure 5: Result with M DIST T H = 1
Figure 6: Result with M DIST T H = 100
M DIST T H is the Mahalanobis distance threshold for data association. It can
be seen from Figure 5 that when the threshold is too small, more points than
reality will be misidentified as landmarks. However, when the threshold is too
large, some landmarks will be ignored, and there will be a large error between
the real position and the estimated position, as shown in Figure 6. Therefore,
an appropriate threshold should be selected to ensure the normal function of
the algorithm. Here we set the threshold to 9.
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2.2.1 Case 1 : a short loop and a dense map with many landmarks
inside the robot perception radius
Figure 7: Result of the case 1
2.2.2 Case 2 : a long loop and a dense map with many landmarks
all along the loop
Figure 8: Result of the case 2
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2.2.3 Case 3 : a long loop and a sparse map with only few landmarks
near the start position
Figure 9: Result of the case 3
When a suitable threshold is chosen, the figures obtained are very similar to
those obtained in question 1. When there are enough landmarks in the sensing
area of the robot, the prediction can be corrected and updated to obtain a good
map, as shown in Figure 7 and 8.
However, when there are not enough landmarks, the black curve (true position) and the red curve (estimated position) no longer coincide, which is also
shown by the large error and uncertainty in the right panel of Figure 9.
3 Probabilistic models
In this section, we keep the configuration with unknown data association
(KNOW N DAT A ASSOCIAT ION = 0), and the environment with large
loops and sparse mapping. According to question 2, the Mahalanobis distance
threshold for data association is set to 9.
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3.1 Question 3 : Change the estimated noise values
3.1.1 Case 1 : Q = Q sim/10, Py = Py sim/10
Figure 10: Result of the case 1
It can be seen from Figure 10 that when Q and P y are smaller than Q Sim
and P y Sim, the location of the landmark will be misidentified. The black
curve (true position) and the red curve (estimated position) do not overlap, and
the error between the real position and the estimated position is very large.
3.1.2 Case 2 : Q = Q sim, Py = Py sim
Figure 11: Result of the case 2
It can be seen from Figure 11, when Q and P y are equal to Q Sim and
P y Sim, we can find that the location of the landmark is better recognized
than before, but it is still not very good. In this case, the error is small, but
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the uncertainty increases. The black curve (true position) and the red curve
(estimated position) overlap better.
3.1.3 Case 3 : Q = 10Q sim, Py = 10Py sim
Figure 12: Result of the case 3
It can be seen from Figure 12, when Q and P y are much larger than Q Sim
and P y Sim, we find that the location of the landmark is well recognized and
the black curve (true position) and the red curve (estimated position) overlap
better. In this case, the error is also small, but the uncertainty is indeed very
large.
3.1.4 Best configuration : Q = 2Q sim, Py = 2Py sim
Figure 13: Best configuration
From Figure 10, Figure 11 and Figure 12, we can find that compared with
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Q Sim and P y Sim, the larger Q Sim and P y Sim, the better the recognition
of landmarks, and the smaller the error between the real position and the estimated position. However, uncertainty has increased. So the best configuration
is that Q and P y are a bit larger than Q Sim and P y Sim. Just like the situation in Figure 13, the landmark positioning is relatively good, the corresponding
uncertainty is relatively small, and the error is not too large.
4 Undelayed initialization
In this section, we will only use the orientation of the landmarks instead of
their distance to perform SLAM. An undelayed initialization method will be
used to initialize several landmarks in the cone corresponding to the perception
direction, and then remove the useless landmarks. This method helps us solve
the problem that it is difficult to estimate their location from a single perception.
4.1 Implementation Sola et al. (2005)
We look for a safe way to fill the conic-shaped ray with the minimum number
of Gaussian-shaped distributions. For that, we define p(s) as a geometric series
with αj = α = constant as (4.1). An overview of the series with its parameters
is shown in Figure 14.
p(s) = X
Ng
j=1
ciΓ(s − β
j−1
s1,(β
j−1σ1)
2
) (4.1)
Figure 14: The conic Ray: a geometric series of Gaussian distributions
From the bounds [smin, smax], and the choice of the ratio α and the geometric
base β, we need to determine the first term (s1, σ1) and the number of terms
Ng. We impose the conditions s1 − σ1 = smin and sNg + σNg ≥ smax to get
(4.2).
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s1 = (1 − α)
−1
smin
σ1 = αs1
Ng = 1 + ceil 
logβ

1 − α
1 + α
smax
smin 
s (4.2)
where ceil(x) is the next integer to x and the geometric base β determines
the sparseness of the series.
Then we want to choose the Gaussian in the ray that best represents the
real landmark, while using at the same time the angular information this ray
provides. It consists of three main operations: the inclusion of all the members
of the ray into the map; the subsequent updates using Federated Information
Sharing; and the successive pruning of bad members. We update the landmark
hypothesis by stacking iteratively one by one according to (4.3) and (4.4), and
the related index has also changed.
Xˆ + =







1
p
.
.
.

Ng
p





(4.3)
P
+ =






P P1
pX
T
· · · P
Ng
pX
T
P1
pX P1
pp
.
.
.
.
.
.
P
Ng
pX P
Ng
pp






(4.4)
4.2 Result
Figure 15: Result of the undelayed initialization
As shown in Figure 15, we have completed the test of the algorithm of bearing
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only SLAM. The appearance and update process of the detected landmarks are
clearly displayed.
References
Sola, J., Monin, A., Devy, M., and Lemaire, T. (2005). Undelayed initialization in bearing only slam. In 2005 IEEE/RSJ International Conference on
Intelligent Robots and Systems, pages 2499–2504.
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