lidar robot
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 1 TEC Oyur
ACKNOWLEDGEMENT
Before I go into thick of the things I would like to add a few heartfelt words
for the people who were part of this endeavor in numerous ways. I am grateful to Mr.Shafeek
A S (Head of the Department, Electronics and Communication) for his valuable advice and
help given during the entire duration of the seminar. I also thank for his guidance, technical
advice and help rendered.
I also remembered the help given by all the faculty of department of
electronics and communication. I would also like to extend my thanks to Mr. Bijith Basher
and Mr .Balraj S (Assistant professor in Electronics and Communication). I also wish tothank our classmates for their help and comments on selected portions of our seminar.
Last but not least I would like our parents who were always there with their
loving advices and suggestions throughout the completion of our effort.
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On Solving Mirror Reflection in lidar Sensing
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ABSTRACT
This paper presents a characterization of sensing failures of light detection and
ranging (LIDAR) given the presence of a mirror, which are quite common in our daily lives.
Although LIDARs play an important role in the field of robotics, previous research has
addressed little regarding the challenges in optical sensing such as mirror reflections. As light
can be reflected off a mirror and penetrate a window, mobile robots equipped with LIDARs
only may not be capable of dealing with real environments. It is straightforward to deal with
mirrors and windows by fusing sensors of heterogeneous characteristics. However, in
distinguishability between mirror images and true objects makes the map inconsistent with
the true environment, even for a robot with heterogeneous sensors. We propose a Bayesian
framework to detect and track mirrors using only LIDAR information. Mirrors are detected
by utilizing the property of mirror symmetry. Spatiotemporal information is integrated using
a Bayesian filter. The proposed approach can be seamlessly integrated into the occupancy
grid map representation and the mobile robot localization framework, and has been
demonstrated using real data from a LIDAR. Mirrors, as potential obstacles, are successfully
detected and tracked.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 3 TEC Oyur
CONTENTS
Chapter No TITLE Page No
ACKNOWLEDGEMENT
ABSTRACT
1 INTRODUCTION 5
2 BACKGROUND NOISE 6
3 SENSOR FUSION 9
4 MIRROR DETECTION 11
4.1 PREDICTION 11
4.2 VERIFICATION 13
4.3 REPRESENTATION 14
5 MIRROR TRACKING 16
5.1 LINE UPDATE 16
5.2 ENDPOINT UPDATE 17
5.3 COMPLEXITY ANALYSIS 18
6 EXPERIMENTAL RESULTS 19
6.1 MAPPING LOCALIZATION &NAVIGATION 19
6.2 QUANTITATIVE EVALUATION 21
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Chapter No TITLE Page No
DVANTAGES 25
DISADVANTAGES 26
APPLICATION 27
CONCLUSION 28
FUTURE SCOPE 29
REFERENCE 30
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 5 TEC Oyur
CHAPTER 1
INTRODUCTION
Simultaneous localization and mapping (SLAM) is the process by which a
mobile robot can build a map of the environment and, at the same time, use this map to
compute its location. As the SLAM problem has attracted immense attention in the mobile
robotics literature, a large variety of sensors have been used for SLAM, such as sonar, lightdetection and ranging (LIDAR), IR, monocular vision, stereo vision, and GPS. The past
decade has seen rapid progress in solving the SLAM problem, and LIDARs are at the core of
most state of-the-art robot systems, such as Boss and Stanley, and the autonomous vehicles in
the Defense Advanced Research Projects Agency (DARPA) Urban Challenge and Grand
Challenge. Because of their narrow beamwidth and fast time of flight, LIDARs are
appropriate for high-precision applications in the field of robotics.
A LIDAR estimates the distance to a surface by measuring the round-trip time
of flight of an emitted pulse of light. Only a fraction of the photons emitted by the LIDAR
are received back through the sensors optics, with this amount being a strong function of the
reflectivity of the object being imaged. White surfaces reflect a large fraction of light, while
black surfaces reflect only a small amount. Transparent objects such as glasses often refract
the light, and a LIDAR measurement of such a surface typically results in the range
information for the object behind the transparent surface. In addition, the mirror-like
reflection of light, in which light from a single incoming direction is reflected into a single
outgoing direction, is called specular reflection or regular reflection. Mirrors are very flat
surfaces and reflect nearly all incident light such that the angles of incidence and reflection
are equal.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 6 TEC Oyur
In this paper, the problem of mirror reflection is addressed. The main
contribution of this study is to provide a solution to detect and track mirrors using only
LIDAR information. The mirror detector utilizes the geometric property of mirror symmetry
to generate hypothetical mirror locations. An identified mirror location is represented using a
line model with endpoints. The mirror tracker is then used to integrate the potential mirror
locations temporally using a Bayesian filter. A Bayesian framework is introduced to the
mobile robot mapping and localization process so that the mirror images can be eliminated.
The proposed approach has been demonstrated using real data from the experimental
platform equipped with a SICK LMS 291 LIDAR. The performance of the proposed
approach has also been evaluated using real data. The ground truth is obtained using another
LIDAR that can observe the actual boundary of a mirror. The ample experimental results
demonstrate the feasibility and effectiveness of our approach.
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On Solving Mirror Reflection in lidar Sensing
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CHAPTER 2
BACKGROUND
Current LIDARs are a standard sensor for both indoor and outdoor mobile
robots, given their inherent reliability. The data from a LIDAR include the angles and the
distances to the objects in the field of view. Compared with LIDARs, vision sensors require
complicated and error-prone processing before obtaining depth information. Range sensors
such as sonar sensors and IR sensors are not capable of fine angular resolution. As a result,
LIDARs are capable of fine angular and distance resolution, real-time data retrieval, and low
false rates.
As light can be reflected off a mirror and penetrate a window, mobile robots
equipped with LIDARs only may not be capable of dealing with real environments. The
sonar, oppositely, is capable of detecting those objects that a LIDAR can miss. The main
drawbacks in sonar sensing are specularity, wide beam width, and frequent misreading due to
either external ultrasound sources or crosstalk. In optical sensing, specular reflection cancause loss of data and noisy signals in optical scans.
Several new LIDAR systems have been introduced recently. A time-of-flight
camera is a 3-D LIDAR that can provide immediate depth images. It enables a diverse set of
emerging medical, biometric, and robotics applications. Several small LIDARs have been
introduced for indoor use, and have a reasonable price and low power consumption. They
operate at high data rates with approximate millimeter resolution. As the development of
LIDARs is getting more and more mature, prices are greatly reduced. Robots also rely more
and more on laser sensing. However, new LIDARs also suffer from the problems of mirror
reflection and glass transparency.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 8 TEC Oyur
Making robots fully autonomous in a wide variety of environments is difficult,
especially in environments with transparent objects, light-reflected objects, or light-absorbed
objects. To make robots fully autonomous in environments with mirrors and windows,
detection and modeling of these objects are critical. The objective is the extraction of sonar
range readings, which are complementary to corresponding laser range information in the
sense that they provide additional environmental information. The LIDAR information is
used to verify corresponding sonar range information. A collection of sonar measurements is
acquired to obtain a dense range map. The laser sensing is used to complement the sonar
sensing by accurately pinpointing the corners and the borders of objects, where the sonar data
are ambiguous. Both of these works proposed to extract complementary sonar readings to
detect those objects not seen by LIDARs. However, the in distinguishability between mirrors
and windows makes robot exploration problematic.
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CHAPTER 3
SENSOR FUSION
In order to demonstrate the ambiguities that arise in a conventional sensor
fusion approach, we maintain two individual occupancy grid maps accumulated from a
LIDAR and a sonar array, respectively. Instead of making hard decisions at every time step,
the occupancy grid maps are utilized to accumulate the temporal information of the sensor
readings. Let Mland M
sbe the occupancy grid maps built using data from a LIDAR and a
sonar array, respectively. Each grid cell (x,y) is determined as a potential obstacle if the
following inequalities
Ml x,y s (2)
Where land
sare predefined probabilities. The values of
land
scan be
obtained according to the apriori probabilities used in the occupancy grid map
representation. In our experiments, l is 0.05 and s is 0.95. At every time step, the sensor
fusion map is calculated accordingly. The probability Mx,y of the grid cell (x,y) in the sensor
fusion map M is Msif (1) and (2) hold; otherwise, M
l.
Fig. 1 visualizes the resulting grid maps using data collected in an
environment with mirrors and windows. Fig. 4(a) and (b) depicts the occupancy grid maps
built using data from a LIDAR and a sonar array, respectively. It can be observed that
mirrors and windows are objects that are likely to be seen by sonar sensors, but less likely to
be identified by LIDARs. Fig. 4(c) shows the sensor fusion map in which most of the mirror
and window locations are successfully identified, in contrast to the LIDAR-only map. Fusion
of heterogeneous sensors is important for collision-free navigation in real environments.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 10 TEC Oyur
Fig 1. Occupancy grid maps
To deal with the problem of mirror reflection, conventional approaches might
include the use of sonar to detect obstacles unseen by a LIDAR. However, it still fails to
resolve the ambiguity of whether an obstacle is specifically a mirror or a window. The
interpretation of an object that appears to be behind the obstacle can be ambiguous. In order
to ensure collision-free navigation and reliable localization capability, having a consistent
understanding of the environment is important. We take advantage of the property of mirror
symmetry to resolve the ambiguity, and use the Bayesian framework to incorporate spatial
and temporal information. By investigating the spatial symmetry of the environment and
using only LIDAR information, our approach can identify mirrors, estimate their locations,
and properly interpret the mirror images of objects.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 11 TEC Oyur
CHAPTER 4
MIRROR DETECTION
In this section, we describe a method to identify potential mirror locations
within a laser scan. We assume that mirrors are planar. A distance-based criterion is used to
determine gaps in a laser scan. The geometric property of mirror symmetry is exploited to
restore the spatial information of reflected scan points. The likelihood field sensor model isapplied to calculate the likelihood that a gap is indeed a mirror. A mirror prediction is then
represented by a Gaussian. The iterative closest points (ICPs) algorithm is utilized for
evaluating the uncertainty of a mirror prediction.
4.1 PREDICTION
The mirror prediction method utilizes the fact that mirrors are usually framed,
i.e., mirrors are physically bounded. For instance, in Fig. 2.1, the mirror is enclosed by a
wooden frame, whereas in Fig. 2.2, the mirror that is supported by a pillar is framed with
steel. The assumption can fail when a mirror that is not placed along anything else does not
have a boundary. First, we assume environments are smooth and define that gaps are
discontinuities of range measurements within a laser scan. Letting z be an observation
containing range measurements taken from a LIDAR, a gap Gij consists of two
measurements {zi,zj|1 i 1}, such that
zi+1 zi >d (3)
zj1 zj >d (4) zk zk+1|d for i
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where n is the cardinality |z| of the observation z, zi is the i-th range
measurement, and d is a predetermined constant. The cardinality of an observation is a
measure of the number of measurements of the observation. In our experiments, d is 1.5 m.
The line with endpoints {pi,pj}is thus considered as a potential mirror location, where pi and
pj are the Cartesian coordinates of the range measurements zi and zj, respectively, in the
robot frame.
Fig 2.1 Fig 2.2
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 13 TEC Oyur
4.2 VERIFICATION
For each gap Gi,j with endpoints {pi,pj}, the measurements {zi+1,zi+2,...,zj1}are
restored in accordance with the geometric property of mirror symmetry. Let ei,j be the line
with endpoints pi and pj, e0,k be the line with endpoints pk and the origin 0, and pi,j,k be the
intersection point between the two lines ei,j and e0,k. The reflected scan point pk with
respect to the kth range measurement zk is calculated such that the angle function calculating
the angle between vectorsp1p2 and
p3p2. The process is illustrated in Fig
(0,pk)= (0,pi,j,k)+(pi,j,k,pk) (6)
(0,pi,j,k,pi)= (pj,pi,j,k,pk) (7)
Fig 3
where (,)is the Euclidean distance function and (p1,p2,p3)
The likelihood i,j of the reflected scan points {pi+1,pi+2,...,pj1} with respect to the local
map around the robot is then calculated using the likelihood field sensor model. A gap Gi,j
with likelihood i,j greater than or equal to is considered likely to be a mirror Mi,j, is a
predefined constant probability. In experiments 0.5, meaning that a gap with at least 50%
confidence is considered as a possible mirror location.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 14 TEC Oyur
4.3 REPRESENTATION
To incorporate temporal integration, a mirror location has to be represented
properly so that the uncertainty can be taken into account. Intuitively, a mirror location is a
line segment and can be described with its endpoints. A filtering algorithm updates the two
endpoints with the associated mirror measurement separately. However, whether a laser
beam is reflected back or reflected off is highly relevant to the smoothness of the mirror
surface and the angle of incidence. The distance between the endpoints of a mirror prediction
is never longer than the true distance. Accompanying the basic light property, the observed
endpoints are, almost surely, not the true endpoints of the mirror. The instability of the
measurements around mirrors are illustrated in Fig. 6. Instead of storing the endpoints of a
mirror measurement directly in the state vector, we propose to represent the mirror with a
line model and store the corresponding endpoints separately. In Section V, this property will
be further used to facilitate the process of estimating the endpoints of a mirror.
We propose to represent mirrors as line segments. In the state vector, a line
segment is represented by the angle and the distance of the closest point on the line to the
origin of the robot frame. The endpoints of a line segment are not placed within the state
vector, but stored separately. The mean vector of the line segment of Mi,j with respect to the
robot frame is given as
RMi , j = _RMi , jRMi , j _ =arctan _ yi , j , kxi , j , k__ x2i,j,k+ y2i,j,k
where x i,j,k and y i,j,k are the xy coordinates of the closest point on the
line to the origin. Image registration is the process of transforming the different sets of data,
acquired at different times or from different perspectives, into one coordinate system. We
propose to exploit the ICP algorithm to estimate the uncertainty of a mirror prediction. By
matching the reflected scan points with the whole laser scan, the displacement, including
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On Solving Mirror Reflection in lidar Sensing
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Translation and rotation, between the reflected scan points and the
environment is calculated. However, adjusting the four parameters of a mirror prediction,
two parameters for the line model and two parameters for the endpoints, using the three
parameters of the displacement is infeasible. Note that a point on a line has 1 DOF. Instead of
using the registration result to refine a mirror prediction, the displacement is utilized to
calculate the covariance matrix of a mirror prediction, which can be expressed as
RMi , j = _2+2 00 2 +x2 +y2
Where and are predetermined values of the measurement noise for the
covariance matrix, and x, y, and are the registration results using the ICP algorithm by
which {pi+1,pi+2,...,pj1} and the whole laser scan arealigned. The values of and
can be obtained by taking into account the modeled uncertainty sources. In our experiments,
is 3
and is 0.2 m. Fig.4 illustrates the mirror detection results in which the gaps in the
laser scans are identified.
Fig 4 ( (a) and (b) is the same as that shown in Fig. 2 and the scene of (c) is the same
as that shown in Fig. 2.1 The robot is at the origin and heads toward the positive x-axis. Dots
are the raw range measurements, where the heavy dots (in red) are the measurements not
identified as the mirror).
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CHAPTER 5
MIRROR TRACKING
In this section, we describe a method to update mirror locations for temporal
integration. Bayesian filtering is a general probabilistic approach for estimating an unknown
probability density function over time using a mathematical process model and incoming
observations. Mirror predictions at different time steps are integrated using an extended
Kalman filter (EKF), which is inherently a nonlinear Bayesian filter. As the endpoints are not
stored in the state vector, the update stage is separated into two stages: the line update stage
and the endpoints update stage. The line update stage integrates mirror predictions tem-
porally using EKFs. The endpoints update stage updates the endpoints of a mirror by
exploiting the basic light property.
5.1 LINE UPDATE
The mean vector and the covariance matrix of a line model are first
transformed into global coordinates, which are given as
Mi , j = _ RMi , j+ txtcos _RMi , j+ t_+ytsin _RMi , j+ t__ (10)Mi ,
Where Jxtand JM
i,jare the Jacobian matrices of the line model with respect to
the robot pose xt =( xt yt t )
T
and the line measurement, respectively, and Pt is the
covariance matrix of the robot pose. Data association is implemented using a validation gate
defined by the Mahalanobis distance. The standard EKF process is then applied to update the
mean vector and the covariance matrix of a mirror estimate.
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5.2 ENDPOINT UPDATE
After the line model of a mirror estimate is updated, the endpoints of the
mirror should be updated accordingly. Let Mt
be j the updated mirror estimate at time t, Mt+1
be the associated mirror measurement at time t + 1, {pti, ptj} and {pt+1u, pt+1v} be the
endpoints of M ti,j andMt+1u,v , respectively, M t+1 be the updated mirror estimate at time
t + 1, and et+1 be the corresponding line model of the updated mirror estimate. We can
compute the point set P = {pti, pt. pt+1u, pt+1v}, which includes the closest points from
the points in P = {pti, ptj, pt+1u, pt+1v} to the line et+1. The process is illustrated in Fig 5.1.
As described in Section IV-C and illustrated in Fig. 5.2, the observed
endpoints of a mirror are usually not the true counterparts, and thus, the distance between the
endpoints of a mirror prediction is never longer than the true distance. We take advantage of
the a priori knowledge to accommodate this phenomenon. The endpoints of the mirror
estimate M t+1 are obtained by finding pair of points in P, such that the distance between
these two points is maximum, which can be expressed as
pt+11, pt+12 _ = argmaxp1, p2 P (p1, p2) (12)
Where pt+11 and pt+12 are the resulting endpoints of the mirror estimate M
t+1. Fig. 9 illustrates a mirror tracking result in which a mirror is correctly detected and
tracked. As can be seen from Fig. 4(c), although the mirror detected with sensor fusion is
spatially sparse, the proposed approach can accurately estimate the location of the mirror.
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Fig 5.1 (Endpoints update. The dashed line shows the updated line model et+1 of a
mirror at time t+ 1. The solid lines indicate the line model of the updated mirror estimate Mti,j and the associated mirror measurementMt+1 u ,v . The thick lines show the corresponding
line segments of M ti,j and Mt+1u ,v . The set P can be computed accordingly whichcontains the closest points from M ti,j andMt+1u ,v to the lineet+1.)
5.3 COMPLEXITY ANALYSIS
The mirror tracker requires O(1) operations in the general case and O(|z|) in
the worst case, where |z| denotes the cardinality of the observation z, as shown in Fig 5.1 The
line update stage takes constant time to perform an EKF update for each of the mirror
estimates. The endpoints update stage also takes constant time to update the endpoints of a
mirror. There are O(|z|)mirror estimates in this stage. Similarly, as there are usually only a
couple of mirrors around an environment, the number of mirror estimates in the line update
stage and the endpoints update stage can be bounded by some constant. The overall time
complexity in the general case is greatly reduced to O(1), which is sufficient for real-time
applications.
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CHAPTER 6
EXPERIMENTAL RESULTS
6.1 MAPPING, LOCALIZATION, AND NAVIGATION
First, we describe the mapping, localization, and navigation problems in
environments with mirrors. Without the mirror detection and tracking process, mirror imagesare considered as parts of real environments. As an occupancy grid map represents the
configuration space (C-space) of a robot, the inconsistency between the real environment and
the map containing mirror images makes the robot navigation problematic. Robots should be
capable of figuring out mirror locations and avoid entering the fake areas formed due to
mirror reflection. To deal with the phenomenon of mirror reflection, the mirror images within
a map have to be detected and corrected accordingly.
In this paper, mirrors are detected and tracked while the SLAM process is
performed. The map is further refined by incorporating mirror information such that mirror
images are eliminated. Accompanying the post processing process, each measurement
perceiving the distance between the robot and a mirror is updated as the distance to the
mirror surface Fig.6 illustrates the post processing process. In Fig. 6(a) the maps built-in
environments with mirrors are shown. The mirror locations, which are estimated while the
robot drove by, are also visualized. As can be seen, the maps that contain mirror images are
inconsistent with the real environments. In Figs. 6(a) and 6(b), the maps incorporating the
mirror information are depicted. Mirror images are eliminated by correcting LIDAR
measurements affected by mirrors.
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The false estimates are removed probabilistically by discarding uncertain
mirror estimates. With the use of the proposed mirror detection and tracking process, the map
can be estimated consistently without apriori knowledge of mirror locations. For mobile
robot localization, such as EKF localization, Markov localization, and Monte Carlo
localization, compared to the post processing process, the preprocessing process is required
to take the mirror information into account. The preprocessing process eliminates the mirror
image within a laser scan by applying the property of mirror symmetry, as described in
chapter 5. The updated LIDAR measurements are then used to perform the localization task.
Fig 6. (Mirror tracking. The scene is the same as that shown in Fig. 3, where the
robot is at around place B. The maps are depicted with respect to a global coordinate system.
The occupancy grid map of the environment is shown, where the rectangle (filled with blue)indicates the robot pose, the lines (in red) are the line models of the mirrors, the ellipses (in
green) show the 2 covariance of the line models, and the thick lines (in red) indicate the
mirror locations. (a) Mirror tracking result. (b) Enlargement of (a). (c) Enlargement of (b))
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6.2 QUANTITATIVE EVALUATION
The feasibility of the proposed algorithm has been demonstrated using real
data. Furthermore, we present a performance analysis of the proposed algorithm. In this
experiment, the SICK LMS 100 LIDAR is used whose angle of view is 270
.As ground truth
mirror locations are usually unobtainable; markers are placed at the boundary of the mirror.
Two LIDARs are used to collect data whose observations are parallel to each other, as shown
in Fig. 12. While one perceives a mirror image, the other can obtain the ground truth mirror
location by observing the markers placed alongside. The two LIDARs are calibrated by
calculating the mean of the displacements from matching empirical observations.
To quantify the performance, we perform SLAM using data from the two
LIDARs separately. There are seven datasets collected around the environment shown in Fig.
7.1. Each dataset contains about 500 observations. The ground truth mirror locations are
annotated and taken into account in the mapping process for obtaining consistent mirror
locations in global coordinates. The maps can be slightly different from each other due to
various noise sources. The resulting maps are aligned for a fair comparison and used to
calculate the estimation error. Fig. 7.2 illustrates the calibrated observations and the resulting
maps obtained from the LIDARs. As the maps are similar, only one estimated map and one
ground truth map are depicted in Fig. 7.2(e) and (f).
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Fig 7.1 (The case in which the LIDAR sees the robot itself in the mir-generated when
the robot sees itself. By adopting the Bayesian ror is also illustrated in Fig. 13(b) and (d). As
can be seen, the framework, it is eliminated naturally from temporal integra-LIDAR can
detect a mirror when the angle of incidence of the tion of observations. The resulting mirror
estimate is shown in emitted photon is zero.)
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We define the overall error of a mirror estimate as the sum of the residuals
between the estimated endpoints and the true endpoints, and define the angular error of a mir-
ror estimate as the angular misalignment between the estimated line model and the true line
model. Root-mean-squared error (RMSE) is used to evaluate the accuracy of our algorithm.
In the experiment, the overall error is 0.12 m and the angular error is 0.47
. The majority of
the error tends to be in the plane of the wall, and the angular misalignment of the estimated
Mirror location is small. This is mainly because of the instability of LIDAR measurements
around a mirror. The predicted locations of the endpoints depend on whether the emitted
photon is reflected back, reflected off, or missing. Mirror reflection can make the observed
endpoints ambiguous. However, a LIDAR that offers high precision can provide accurate
angular estimates of mirrors. Note that the error includes uncertainties from the SLAM
process. Just as with solving the SLAM problem, the performance also depends on sensor
characteristics and the environment. The experiment shows that the proposed approach is
effective, even though various noise sources are involved. The results in the experiments fig
7.2 shown below
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Fig 7.2 (Observations from the LIDARs. (a)(d) Robot is shown by the rectangle (in black)
and heads toward the positive x-axis. Dots are the range measurements containing mirror
images, where the heavy dots (in red) are the measurements not identified as the mirror
images, and the light dots (in cyan) are the measurements with false range information due to
mirror reflection. Lines (in black) indicate the detected mirror locations. Circles (in green)
are the range measurements used for performance evaluation. (e) Estimated map is shown in
which the thick (red) line indicates the mirror location. (f) Ground truth map is depicted, )
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ADVANTAGES
High speed response If the environment is opaque.
Due to the use of LIDAR the captured images have high resolution
It can detect contaminants in Sewages having radioactive particles
It have high effective edge detecting capability
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DISADVANTAGES
There is time delay for the scanning of glass environments.
The LIDAR robot cannot detect obscured anti silicon pirate glasses
LIDAR robots need high power servo motor because of the heaviness of LIDAR
Transceiver
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APPLICATION
It can used as pipe line and sewage maintains
It can used as a land mapper in various environment
The LIDAR implemented Robot can work in hazards environments
It can use as a navigator robot.
It can used as fire extinguisher
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 28 TEC Oyur
CONCLUSION
Making robots fully autonomous in a wide variety of environments is difficult.
To our best knowledge, the solution to the problem of mirror reflection has not been
addressed previously. The primary contribution of this paper is to introduce the mirror
detection and tracking framework using only LIDAR information. The mirror detectionmethod utilizes the property of mirror symmetry to calculate the confidence of a mirror pre-
diction. The image registration technique is used for evaluating the uncertainty of a mirror
prediction. The proposed endpoints update strategy employs the fact that the distance
between the endpoints of a mirror prediction is never longer than the true distance. The
proposed approach can be seamlessly integrated into the mobile robot localization framework
and the occupancy grid map representation. The ample experimental results using real data
from a LIDAR have demonstrated the feasibility and effectiveness of the proposed approach.
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On Solving Mirror Reflection in lidar Sensing
Dept. of ECE 29 TEC Oyur
FUTURE WORK
In this paper, we use a heuristic method to guess possible mirror locations in
the continuous Cartesian space. It relies on the fact that mirrors are usually framed or placed
along a wall. If the boundary of a mirror is not apparent or the mirror is not placed along
anything else, the proposed approach will fail. Sensor fusion is versatile in its capability to
deal with diversified surfaces, but less precise. On the other hand, the major drawback of
LIDAR-only approaches can be their incapability to detect transparent objects, due to the
nature of light. Future work will include an approach to guess the possible mirror locations
using sensor fusion. Because of the inaccuracy of sonar readings, the extraction and
reconstruction of disjointed line segments is required to generate a mirror prediction. in
distinguishability between mirrors and windows in sensor fusion can also be resolved
through the use of sensor fusion and the proposed mirror detection and tracking process. In
addition, it would also be of interest to study some of the special cases: multiple reflections
of mirrors, curved mirrors, and mirror symmetric scenes.
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On Solving Mirror Reflection in lidar Sensing
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