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Book ChapterDOI

Online Reconstruction Of Vehicles In A Car Park

01 Jan 2006-Vol. 25, pp 207-218

TL;DR: A method of obtaining vehicle hypothesis based on laser scan data only is proposed and implemented on the robotic vehicle, CyCab, for navigation and mapping of the static car park environment.
Abstract: In this paper, a method of obtaining vehicle hypothesis based on laser scan data only is proposed This is implemented on the robotic vehicle, CyCab, for navigation and mapping of the static car park environment Laser scanner data is used to obtain hypothesis on position and orientation of vehicles with Bayesian Programming Using the hypothesized vehicle poses as landmarks, CyCab performs Simultaneous Localization And Mapping (SLAM) A final map consisting of the vehicle positions in the car park is obtained

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Online Reconstruction Of Vehicles In A Car Park
Christopher Tay, Cédric Pradalier, Christian Laugier
To cite this version:
Christopher Tay, Cédric Pradalier, Christian Laugier. Online Reconstruction Of Vehicles In A Car
Park. Proc. of the Int. Conf. on Field and Service Robotics, Jul 2005, Port Douglas Australia,
Australia. pp.207-218, �10.1007/978-3-540-33453-8_18�. �inria-00182042�

On-Line Reconstruction of Vehicles In A Car
Park
Christopher Tay Meng Keat
1
, edric Pradalier
2
, and Christian Laugier
3
1
INRIA Rhˆone Alpes GRAVIR Laboratory tay@inrialpes.fr
2
CSIRO ICT Center, Canberra-Australia cedric.pradalier@csiro.au
3
INRIA Rhˆone Alpes GRAVIR Laboratory christian.laugier@inrialpes.fr
Summary. In this paper, a method of obtaining vehicle hypothesis based on laser
scan data only is proposed. This is implemented on the robotic vehicle, CyCab,
for navigation and mapping of the static car park environment. Laser scanner data
is used to obtain hypothesis on position and orientation of vehicles with Bayesian
Programming. Using the hypothesized vehicle poses as landmarks, CyCab performs
Simultaneous Localization And Mapping (SLAM). A final map consisting of the
vehicle positions in the car park is obtained.
Key words: Vehicle Detection, Bayesian Programming
1 Introduction
In the framework of automatic vehicles in car parks, a 2D map of a car park
is constructed using the autonomous vehicle, CyCab, as the experimental
platform. The map of the car park will contain the positions and orientations
of the different vehicles in the car park. An application of such a map is to
serve as a reference to indicate obstacle positions . Furthermore, it can indicate
the state of the parking lots in the car park, and possibly be used in higher
level applications such as automatic parking.
Several object based representation of the environment were proposed.
Chatila et al. [8] represented the map with a set of lines. More advanced
methods in mapping consists of approximating the environment using small
polygons [3] [4]. Such methods used a variant of the Expectation-Maximization
to generate increasingly accurate 3D maps as more observations are made.
In this paper, a higher level of representation (vehicles in this case) of the
environment is used instead of fundamental geometrical entities.
Currently, the CyCab robotic vehicle localizes itself in a static environemnt
with respect to artificially installed reflective cones. This localization serves to
build a grid map of the environment and has the capability to perform motion
planning with safe navigation as described in [1]. However, reflective cones as

2 Christopher Tay Meng Keat, edric Pradalier, and Christian Laugier
artifical landmarks is not a very practical approach. An improvement from an
application point of view is to use naturally occuring objects often found in
the car park as landmarks. In this paper, vehicles found in the car park are
used.
The general idea is to use only the laser data without artificial or predefined
landmarks, CyCab will navigate the car park autonomously while generating
a map of its environment.While CyCab is travelling around the car park,
scanning the environment, CyCab continuously reads in odometric and laser
data. At each stage of the iteration, CyCab estimates its own position and
orientation of the form (x, y, θ) and creates a map of the car park in the world
frame of reference. The origin of the world frame of reference is taken from
the initial position of CyCab. The map is then represented as a set of tuples,
each containing the position and orientation of the vehicles detected. CyCab
hypothesizes the configuration of the vehicles in the surrounding based on the
laser scan inputs from laser scanner only.
The advantage of the approach is its ability to map any car park without
installing any external aids. With the set of vehicle poses representing the
map, a compact and semantic representation of the map can be obtained.
2 System Overview
The mechanism of the entire system can be broken down into three funda-
mental components, vehicle detection, the simultaneous localization and map-
ping(slam) and the map construction . CyCab is provided with two types of
raw data, the laser scans and CyCab’s odometric data. Figure 1 shows the
block diagram of the different stages and its interactions:
Data
Laser
Raw
Positions
Vehicle
Relative
Positions
Vehicle
Absolute
Map
Hypothesized
Robot Localization
Vehicle Odometry
Detection
Vehicle
SLAM
Map
Construction
Fig. 1. Overview showing the mapping process
1. Vehicle Detection: With only raw laser scan data, vehicle detection
constructs hypotheses about the positions and orientation of vehicles in
the car park.
2. SLAM: Coupled with information about odometry of CyCab, SLAM
module makes use of the constructed vehicle hypotheses as landmarks

On-Line Reconstruction of Vehicles In A Car Park 3
to localize itself. With its own configuration, SLAM can then calculate
the absolute configuration of the vehicles with respect to real world coor-
dinates.
3. Map Construction: The hypotheses of vehicles have to be checked for
inconsistencies. It is possible for the hypotheses obtained to conflict with
a previous corresponding hypothesis. Furthermore, multiple hypothesis
SLAM methods such as FastSLAM produces a set of hypotheses, a map
construction module is required to merge the information from the differ-
ent hypotheses to obtain the final map.
3 Vehicle Detection
Vehicle detection is the process of forming possible vehicle hypotheses based
on the laser data readings. This problem is addressed in this paper using
bayesian programming[2]. Bayesian programs provides us with a framework
for encoding our a priori knowledge on the vehicle to infer the possible vehicle
poses. In this case, the a priori knowledge consists of the length and width
of vehicles which is assumed to be the same across standard vehicles (cars)
and that the type of vehicles in the car park is of the same class. The subtle
differences in the size of the vehicles can be accomodated for in the bayesian
paradigm and this property renders our assumption practical.
The detection of vehicles takes place in two stages. The first stage is basi-
cally composed of three portions:
1. Clustering and segmentation, to group a set of points indicating ob-
jects, using a distance criterion. Next, segments are obtained using clas-
sical split and merge techniques.
2. Vehicle hypotheses construction using bayesian programming. The
construction of hypotheses by bayesian programming results in a mech-
anism similar to that of hough transform. Peak values in the histogram
indicates the most probable vehicle poses.
With real data, the first stage produces too many false positives. A second
stage of filtering is applied to each vehicle hypothesis obtained after the first
stage as a form of validation gating in order to reduce the number of false
positives. It is broken down into two portions:
1. Edge Filtering is applied to extract the set of line segments that is only
relevant to the vehicle hypothesis in question.
2. Vehicle Support Filtering is based on our proposed metric, vehicle
support, that measures how much of the two adjacent sides of a vehicle
are seen. We try to remove as many false positives as possible using the
vehicle support.

4 Christopher Tay Meng Keat, edric Pradalier, and Christian Laugier
3.1 Construction of vehicle hypotheses
A bayesian program is used to infer vehicle positions. The formulation of our
bayesian program results in a mechanism similar to that of a hough transform.
We can infer on positions and orientations of vehicles from segments detected
from laser scan data which is analogous to the way line segments are recovered
from an ensemble of points. However our histogram cells are updated in terms
of probability which takes into account the length and the width of vehicles
instead of voting in the case of hough transform.
Briefly, a bayesian program is composed of:
the list of relavant variables;
a decomposition of the joint distribution over these variables;
the parametrical form of each factor of the decomposition;
the identification of the parameters of these parametrical forms;
a question in the form of probability distribution inferred from the joint
distribution.
In the construction of the histogram, each line segment is treated inde-
pendently. In doing so, it will be sufficient to simply go through the list of
segments and add necessary information into the histogram for each line seg-
ment. This is achieved by performing data fusion with diagnostic [7]. Inference
of vehicle poses is represented by the bayesian program in fig. 2 and fig. 3. In
this paper, the following variables are adopted:
V : A boolean value indicating the presence of a vehicle
Z = (x, y, θ): The pose of a vehicle
S: Ensemble of extracted line segments
M {0, 1}
p
: Compatibility of segments with vehicle pose
C {1, 2}
p
: A value of 1 or 2 if segment corresponds to the width and
length of a vehicle respectively
π: A priori knowledge
To represent the absence of specific knowledge on the presence of vehicle
P (V | π
f
), the pose of the vehicle P (Z | π
f
) and the segments P (S | π
f
), they
are represented as a uniform distribution.
The semantic of the question from the bayesian program (fig. 2) is to
find the probability of a vehicle given all segments and the vehicle pose. This
question can be simplified using baye’s rule:
P ([V = 1] | [M = 1] [S] Z π
f
)
= K
Y
i
P ([M
i
= 1] | [V = 1] Z S
i
π
f
)
With K constant. The simplification of the question gives the product of the
probability of the contributions of each line segment, which is given by each
sensor sub-model. The local maximas of the function, P ([V = 1]|[M = 1] [S =

Citations
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Dissertation
Cédric Pradalier1Institutions (1)
05 Jun 2015-
TL;DR: This document reports on research conducted between 2001 and 2015 in the field of field robotics, specifically in what became known “field robotics”: a focus of robotics on outdoor, little-structured environments close to industrial applications.
Abstract: This document reports on research conducted between 2001 and 2015 in the field of au- tonomous mobile robotics, specifically in what became known “field robotics”: a focus of robotics on outdoor, little-structured environments close to industrial applications. Chap- ter 2 describes a number of research projects, starting with activities initiated during my doctorate research at INRIA between 2001 and 2004, followed by a post-doctoral fellowship at CSIRO, in Canberra and Brisbane, Australia between 2004 and 2007. From 2007 to 2012, my role as Deputy-Director of the Autonomous Systems Lab at ETH Z urich, Switzerland, gave me the opportunity to supervise a number of projects ranging from space robotics and mechatronic design to European projects on indoor navigation or autonomous driving. On the other hand, the last chapter will describe my reseach plan stemming from this experience and preliminary results from projects started in my current position as Associate Professor at GeorgiaTech Lorraine, the French campus of the Georgia Institute of Technology, also known as GeorgiaTech, located in Atlanta, USA

5 citations


References
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01 Jan 2003-
TL;DR: This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map.
Abstract: Simultaneous Localization and Mapping (SLAM) is an essential capability for mobile robots exploring unknown environments. The Extended Kalman Filter (EKF) has served as the de-facto approach to SLAM for the last fifteen years. However, EKF-based SLAM algorithms suffer from two well-known shortcomings that complicate their application to large, real-world environments: quadratic complexity and sensitivity to failures in data association. I will present an alternative approach to SLAM that specifically addresses these two areas. This approach, called FastSLAM, factors the full SLAM posterior exactly into a product of a robot path posterior, and N landmark posteriors conditioned on the robot path estimate. This factored posterior can be approximated efficiently using a particle filter. The time required to incorporate an observation into FastSLAM scales logarithmically with the number of landmarks in the map. In addition to sampling over robot paths, FastSLAM can sample over potential data associations. Sampling over data associations enables FastSLAM to be used in environments with highly ambiguous landmark identities. This dissertation will describe the FastSLAM algorithm given both known and unknown data association. The performance of FastSLAM will be compared against the EKF on simulated and real-world data sets. Results will show that FastSLAM can produce accurate maps in extremely large environments, and in environments with substantial data association ambiguity. Finally, a convergence proof for FastSLAM in linear-Gaussian worlds will be presented.

2,358 citations


"Online Reconstruction Of Vehicles I..." refers methods in this paper

  • ...A detailed description and example based on FastSLAM [5] [6], which is used in this paper, is presented in [9]....

    [...]

  • ...Furthermore, multiple hypothesis SLAM methods such as FastSLAM produces a set of hypotheses, a map construction module is required to merge the information from the different hypotheses to obtain the final map....

    [...]

  • ...It is basically a combined map of all the various hypotheses obtained from FastSLAM....

    [...]

  • ...The current implementation is a naive and unoptimized version of FastSLAM that executes with a frequency of between 4-6Hz on a pentium 4....

    [...]


Proceedings ArticleDOI
28 Jul 2002-
Abstract: The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.

1,808 citations


Proceedings Article
09 Aug 2003-
TL;DR: This paper describes a modified version of FastSLAM which overcomes important deficiencies of the original algorithm and proves convergence of this new algorithm for linear SLAM problems and provides real-world experimental results that illustrate an order of magnitude improvement in accuracy over the original Fast SLAM algorithm.
Abstract: Proceedings of IJCAI 2003 In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM which overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM problems and provide real-world experimental results that illustrate an order of magnitude improvement in accuracy over the original FastSLAM algorithm.

1,036 citations


"Online Reconstruction Of Vehicles I..." refers methods in this paper

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    [...]


Proceedings ArticleDOI
Raja Chatila1, Jean-Paul Laumond1Institutions (1)
25 Mar 1985-
TL;DR: The approach proposed in this paper relies on the use of a multisensory system, favo ring of the data collected by the more accurate sensor in a given situation, averaging of different but consistent measurements of the same entity weighted with their associated uncertainties.
Abstract: In order to understand its environment, a mobile robot should be able to model consistently this environment, and to locate itself correctly. One major difficulty to be solved is the inaccuracies introduced by the sensors. The approach proposed in this paper to cope with this problem relies on 1) defining general principles to deal with uncertainties : the use of a multisensory system, favo ring of the data collected by the more accurate sensor in a given situation, averaging of different but consistent measurements of the same entity weighted with their associated uncertainties, and 2) a methodology enabling a mobile robot to define its own reference landmarks while exploring its environment. These ideas are presented together with an example of their application on the mobile robot HILARE.

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TL;DR: An algorithm for generating compact 3D models of indoor environments with mobile robots using the expectation maximization algorithm to fit a lowcomplexity planar model to 3D data collected by range finders and a panoramic camera is described.
Abstract: This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a lowcomplexity planar model to 3D data collected by range finders and a panoramic camera. The complexity of the model is determined during model fitting, by incrementally adding and removing surfaces. In a final post-processing step, measurements are converted into polygons and projected onto the surface model where possible. Empirical results obtained with a mobile robot illustrate that high-resolution models can be acquired in reasonable time.

180 citations


"Online Reconstruction Of Vehicles I..." refers methods in this paper

  • ...More advanced methods in mapping consists of approximating the environment using small polygons [3] [4]....

    [...]