|
Introduction to the workshop 8:20
Session I: Path Planning & Navigation systems 8:30
Chairman: Urbano Nunes
- Title: : Why can’t road positioning and integrity be friends? 8:30
Keynote speaker: Rafael Toledo-Moreo
(Technical University of Cartagena, Spain)
30min + 5min questions
Related paper,
Presentation
Abstract: Today’s positioning systems work quite well in many situations. However, they lack some robustness, what hinders its exploitation for safety-critical and liability-critical applications. The warranty of the quality of the positioning service would open the way for further services, but for the community of the field it is unclear whether or not integrity is achievable for road positioning. There are several good reasons for that hesitation: The representation of the integrity of such a complex system by means of projecting all its possible errors onto an integrity parameter is difficult; the use of assumptions may cause that a non-modeled event disrupts the consistency of the error estimates; and also, the different ways in which the concept of integrity is approached in the literature lead to confusion and contradictions.
This talk focuses on road positioning and its integrity, discussing aspects that play a role in this problem such as Global Navigation Satellite Systems, aiding sensors, the vehicle environment and its model, data fusion methods and map-matching algorithms.
- Title: Autonomous Navigation in Crowded Campus Environments 9:05
Authors: Z. J. Chong, B. Qin, T. Bandyopadhyay, T. Wongpiromsarn, E. S. Rankin, M. H. Ang Jr., E. Frazzoli, D. Rus, D. Hsu, K. H. Low
17min + 3min questions
Paper,
Presentation
Abstract: This paper considers autonomous navigation in
crowded city environments. An autonomous vehicle testbed is
presented. We address two challenges of pedestrian detection
and GPS-based localization in the presence of high-level build-
ings. First, we augment the localization using local laser maps
and show improved results. A pedestrian detection algorithm
using a complementary vision and laser system is proposed.
We implement this algorithm in our testbed and evaluate its
performance using purely off the shelf components and open
source software packages provided by ROS. We also show
how utilizing existing infrastructural sensors can improve the
performance of the system. Potential applications of this work
include fully automated vehicle systems in urban environments
typical in megacities in Asia.
- Title: Integration of visual and depth information for vehicle detection 9:25
Authors: A. Makris, M. Perrollaz, I. Paromtchik, C. Laugier
17min + 3min questions
Paper,
Presentation
Abstract: In this work an object class recognition method
is presented. The method uses local image features and follows
the part based detection approach. It fuses intensity and depth
information in a probabilistic framework. The depth of each
local feature is used to weight the probability of finding the
object at a given scale. To train the system for an object
class only a database of annotated with bounding boxes images
is required, thus automatizing the extension of the system to
different object classes. We apply our method in the problem
of detecting vehicles captured from a moving platform. The
experiments in a data-set of stereo images captured in an urban
environment show a significant improvement in performance
when using both information modalities.
Coffee Break 10:00
Session II: Perception & Situation awareness 10:30
Chairman: Rafael Toledo-Moreo
- Title: Situation awareness & Risk based navigation in dynamic environments 10:30
Keynote speaker: C. Laugier (Inria Grenoble, France)
30min + 5min questions
Paper,
Presentation
Co-Authors: I. Paromtchik, M. Perrollaz, J.D. Yoder, C. Tay, K. Makhnacha, C. Fulgenzi, A. Spalanzani
Abstract: This talk address the problem of safe navigation in dynamic environments, with a focus on intelligent vehicle application.
After a global overview of the problem and of the state of the art, several key aspects of this problem will be addressed: Bayesian perception and sensor fusion, Motion prediction for sensed mobile obstacles (including maneuvers prediction at road intersections), Probabilistic collision risk assessment, and Risk based navigation. Results obtained with our equipped Lexus hybrid vehicle will be presented and discussed.
- Title: From Structure to Actions:
Semantic Navigation Planning in Office Environments 11:05
Authors: K. Uhl, A. Roennau, R. Dillmann
17min + 3min questions
Paper,
Presentation
Abstract: The use of meaning in mapping and navigation
is inevitable if a robot has to interact with its environment in
a goal-directed way. Moreover, a semantic environment model
makes navigation planning more efficient and simplifies the
review and communication of the robot’s knowledge. Existing
work in this area decomposes the environment into places,
which can be distinguished using the robot’s sensors. However,
if important features of the environment cannot be detected by
the robot’s sensors a different approach is needed.
This paper introduces the Semantic Region Map, an envi-
ronment model with complex metric, topological and semantic
features. It shows how navigation points, so-called semantic
positions, can be deduced from the map using a semantic
description of the environment. Furthermore, the semantic
positions are connected to a reachability graph, whose edges
are labelled with robot actions, using a semantic description of
the robot’s capabilities. An ontology consisting of a taxonomy
and a set of rules are used to implement the semantic models.
The concept of the Semantic Region Map is applied to a robot
operating in an office environment.
- Title: Situation Assessment and Trajectory Planning for AnnieWAY 11:25
Authors: C. Stiller, J. Ziegler
17min + 3min questions
Paper,
Presentation,
Video1
Abstract: This contribution addresses machine perception
of a priori unknown environment, situation recognition, and
automated trajectory planning in urban traffic. We discuss
how to represent and acquire metric, symbolic and conceptual
knowledge from video and lidar data of a vehicle. A hardware
and software architecture tailored to this knowledge structure
for an autonomous vehicle is proposed. Emphasis is laid on
methods for situation recognition employing geometrical and
topological reasoning and Markov Logic Networks. Trajec-
tory planning is conducted in spatiotemporal state lattices.
The computational effort of the planning method is almost
independent of the number of moving objects as these simply
disable spatiotemporal nodes. The planning optimizes a quality
measure that considers safety, efficiency, and comfort. Results
are shown from the autonomous vehicle AnnieWAY that is able
to autonomously travel in urban and platooning scenarios.
Lunch break 12:00
Session III: Interactive session 13:30
Chairman: P. Martinet
- Title: Proposition for propagated occupation grids for non-rigid moving objects tracking
Authors: B. Lefaudeux, G. Gate, F. Nashashibi
Paper
Abstract: Autonomous navigation among humans is, however
simple it might seems, a difficult subject which draws a lot
a attention in our days of increasingly autonomous systems.
From a typical scene from a human environment, diverse shapes,
behaviours, speeds or colours can be gathered by a lot of sensors
; and a generic mean to perceive space and dynamics is all the
more needed, if not easy. We propose an incremental evolution
over the well-known occupancy grid paradigm, introducing grid
cell propagation over time and a limited neighbourhood, handled
by probabilistic calculus. Our algorithm runs in real-time from
a GPU implementation, and considers completely generically
space-cells propagation, without any a priori requirements. It
produces a set of belief maps of our environment, handling
occupancy, but also items dynamics, relative rigidity links, and
an initial object classification. Observations from free-space
sensors are thus turned into information needed for autonomous
navigation.
- Title: Probabilistic Road Geometry Estimation using a Millimetre-Wave Radar
Authors: A. Hernandez-Gutierrez, J. I. Nieto, T. Bailey, E.M. Nebot
Paper,
Poster,
Video1,
Video2,
Abstract: This paper presents a probabilistic framework for
road geometry estimation using a millimetre wave radar. It
aims at estimating the geometry of unpaved and unmarked
roads, and also provides the vehicle location with respect to
the edges of the road. This road tracking system employs
a radar sensor due to its robustness to weather conditions
such as fog, dust, rain and snow. The proposed approach is
robust to noisy measurements because the radar target locations
are modelled as Gaussian distributions. These observations
are integrated into a Kalman Particle filter to estimate the
posterior distribution of the parameters that best describe the
geometry of the road. Experimental results using data acquired
on a highway road are presented. The effectiveness of the
proposed approach is demonstrated by a qualitative analysis
of the results.
- Title: Safety robotic lawnmower with precise and low-cost L1-only RTK-GPS positioning
Authors: J.M. Codol, M. Poncelet, A. Monin, M. Devy
Paper
Abstract: In this paper, we will introduce an autonomous
robotic lawnmower, equipped by a safety and low-cost RTK-
DGPS centimetric positioning system available also in semi-urban
environment. The GPS-RTK sensors are a pair of L1-only GPS
receivers (L1-only GPS receivers are cheaper than dual-frequency
ones because of the existence of patents on the usage of the
second frequency). This work is an extension of a collaboration
between NAV ON TIME and BELROBOTICS, consisting on
evaluate GPS replacement for the current mower area limit (a
buried wire). The objective of the latest work is to ensure the
GPS mission realization, keeping the same safety as the buried
wire one. In this context, this paper will present a complete
statistical approach to L1-only RTK-positioning system in urban
environment. The result of this approach have been embedded
into the mower machine, by using a Linux operating system
equipped with an ARM-9 processor running at 400MHz, and
an UHF radio-communication to the reference station, this one
having the role of realize path planning, geographical database
managing, remote and IHM communication.
- Title: Odometry from Planar landmarks
Authors: K. Narayana, B. Steux
Paper
Abstract: This paper presents a new perception odometry
approach using extracted stationary planar features to resolve
5 degrees of freedom of the robot motion. The approach
exploits the geometrical properties of the extracted features
to determine the transformation of the moving robot, which
has perceived these landmarks. This way of localizing can help
several applications in indoors and outdoors such as urban
canyons, with plenty of planar features. The paper presents the
concept and the algorithm, and validates them using a simulated
scenario.
- Title: Probabilistic autonomous navigation using Risk-RRT approach and models of human interaction
Authors: J. Rios-Martinez, A. Spalanzani, C. Laugier
Paper
Abstract: Autonomous transportation in human environ-
ments must follow social conventions. An autonomous
wheelchair, for example, must respect proximity constraints but
also respect people interacting, it should not break interaction
between people talking, unless the user want to interact with
them. In this case, the robot (i.e. the wheelchair) should find
the best way to join the group. In this paper, we propose a
risk-based navigation method which include risk of collision but
also risk of disturbance. Results exhibit new emerging behavior
showing how the robot takes into account social conventions in
its navigation strategy.
Session IV: 2D and 3D Mapping & Localization 14:30
Chairman: C. Laugier
- Title: 2D/3D mapping and localization 14:30
Keynote speaker: C. Stiller (Karlsruhe Institute of Technology)
30min + 5min questions
Presentation,
Video1,
Video2
Co-Authors: A. Geiger, F. Moosmann
Abstract: On-line environment modelling and mapping are of growing
importance for autonomous robots and cognitive automobiles. Emerging from 2D
or 2.5D flat world representations improved sensors and computing
capabilities allow for 3D world representations and estimation of full 6 DOF
robot motion. The choice of fast and expressive features enhances density
while reducing noise. We present 3D dense maps acquired in real time from a
lidar or stereo camera rig mounted on a traveling experimental vehicle
without requiring any additional odometry or inertial sensors.
- Title: A New Strategy for Feature Initialization in Visual SLAM 15:05
Authors: G.Bresson, T. Feraud, R. Aufrere, P. Checchin and R. Chapuis
17min + 3min questions
Paper,
Presentation,
Video1,
Video2,
Video3,
Video4
Abstract: This paper presents a Visual EKF-SLAM process
using an original and very efficient strategy for initializing
landmarks. Usually, with Cartesian coordinates, new points are
created along the line-of-sight with a large variance. However,
this type of initialization is subject to significant linearization
issues making landmarks diverge from their real position. The
immediate consequence is a failure of the Visual SLAM process.
We propose here a new strategy that avoids or drastically limits
the linearization errors. The first part of this strategy takes place
during the tracking process where a coherent window is needed
in order to successfully follow a point and make it converge. The
second part concerns the update step. Due to linearization errors,
a landmark in front of the observer can be updated behind it.
We compute a corrective of the Kalman gain in order to preserve
the integrity. We applied this strategy to real data illustrating its
efficiency.
- Title: Building Facade Detection, Segmentation, and Parameter Estimation for Mobile Robot Localization and Guidance 15:25
Authors: J.A. Delmerico, P. David, J.J. Corso
17min + 3min questions
Paper,
Presentation
Abstract: Building facade detection is an important problem
in computer vision, with applications in mobile robotics and
semantic scene understanding. In particular, mobile platform
localization and guidance in urban environments can be enabled
with an accurate segmentation of the various building facades
in a scene. Toward that end, we present a system for segmenting
and labeling an input image that for each pixel, seeks to answer
the question “Is this pixel part of a building facade, and if
so, which one?” The proposed method determines a set of
candidate planes by sampling and clustering points from the
image with RANSAC, using local normal estimates derived
from PCA to inform the planar model. The corresponding
disparity map and a discriminative classification provide prior
information for a two-layer Markov Random Field model. This
MRF problem is solved via Graph Cuts to obtain a labeling
of building facade pixels at the mid-level, and a segmentation
of those pixels into particular planes at the high-level. The
results indicate a strong improvement in the accuracy of
the binary building detection problem over the discriminative
classifier alone, and the planar surface estimates provide a good
approximation to the ground truth planes.
Coffee break 16:00
Session V: Mobile robot modeling and control 16:30
Chairman: U. Nunes
- Title: Generic algorithm for high accurate trajectory control in different conditions 16:30
Keynote speaker: R. Lenain (Cemagref, France)
30min + 5min questions
Presentation,
Video1,
Video2,
Video3,
Video4,
Video5,
Video6,
Video7,
Video8,
Video9,
Video10,
Video11,
Video12,
Video13,
Video14,
Video15,
Video16,
Video17
Co-Authors: B. Thuilot, C. Cariou, P. Martinet
Abstract: From public transportation to agriculture, many fields of application may benefit from automation in the area of mobile robotics. As a result, research in that topic is subject of more and more investigation in order to propose new systems, from driver assistance (e.g automatic parking...) up to fully autonomous vehicles (such as autonomous robots acting in hazardous environment). In order to be fully effective, these innovations have to be accurate, safe, and able to act in various conditions. Many open problems then need to be addressed in order to propose such innovations in several part. If perception and navigation issues constitute important key points, the problem of motion control remains an important point since the control law to be embedded have to face a variability of conditions impacting directly their behaviour.
These conditions rely on constant parameter, pending on the considered robot or vehicle (mechanical properties, actuators, specifications, …), but also depends on the variable interaction with the environment (grip conditions, terrain geometry, reachable velocity, ...). As a result in order to propose an efficient and accurate motion whatever the conditions variability, control laws have to account of the different dynamics encountered.
This talk investigates the motion control of mobile robot in different conditions through the example of path tracking. It proposes several strategies to preserve the motion accuracy and safety whatever the encountered conditions. A correlation between the reachable velocity and the terrain complexity is proposed to extract the different effects which have to be accounted and related control objective. Based on this classification several modelling and control strategies are illustrated to face the considered phenomena. Starting from classical kinematic controller for simple path tracking task at low speed on flat terrain with good grip conditions, the talk investigates a rising complexity of situation. Adaptive control based on advanced kinematic model is proposed to face low grip conditions. This adaptive control is then associated with predictive control in order to preserve accuracy when increasing the velocity. Limitation of this controller with respect to the increasing speed and safety is pointed out and a new observer mixing kinematic and dynamic representation is proposed. This model permits also to account for 3D motion and permit to investigate the risk of instability rising at high speed. A control law acting on velocity in order to limit the rollover risk is then derived. This notion is then extended in a predictive way to adress the topic of obstacle avoidance and traversability for mobile robots. Finally, the notion of predictive control on velocity is extended to preserve the integrity of mobile robot, i.e, to preserve the stability, the controlability, and the accuracy of motion control. The capabilities of the different algorithm are investigated on actual experiments, using different kind of robots and vehicle, moving on different kind of ground.
- Title: A control strategy taking advantage of inter-vehicle communication for platooning navigation in urban environment 17:05
Authors: P. Avanzini, B. Thuilot, P. Martinet
17min + 3min questions
Paper,
Presentation
Abstract: This paper deals with platooning navigation in
the context of innovative solutions for urban transportation
systems. More precisely, a sustainable approach centered on au-
tomated electric vehicles in free-access is considered. To tackle
the major problem of congestions in dense areas, cooperative
navigation according to a platoon formation is investigated.
With the aim to ensure the formation stability, i.e. longitudinal
disturbances within the platoon do not grow when progressing
down the chain, a global decentralized platoon control strategy
is here proposed. It is supported by inter-vehicle communica-
tions and relies on nonlinear control techniques. A wide range
of experiments, carried out with up to four urban vehicles,
demonstrates the capabilities of the proposed approach: two
localization devices have been tested (RTK-GPS and monocular
vision) along with two guidance modes (the path to be followed
is either predefined or inferred on-line from the motion of the
manually driven first vehicle).
- Title: Semiautonomous Longitudinal Collision Avoidance Using a Probabilistic Decision Threshold 17:25
Authors: J. Johnson, Y. Zhang, K. Hauser
17min + 3min questions
Paper,
Presentation
Abstract: Automated emergency maneuvering systems can
avoid or reduce the severity of collisions by taking control of a
vehicle away from the driver during high-risk situations. The
choice of when to switch to emergency control is challenging
in the presence of uncertain information (imperfect sensors,
road conditions, uncertain object behavior, etc.) and many
dynamic obstacles. This paper considers longitudinal collision
avoidance problems for a vehicle traveling along a known path.
A probabilistic decision threshold framework is presented in
which the user’s control is overridden if the probability that
it would lead the system into an unsafe state exceeds some
threshold.We apply the technique to collision imminent braking
for obstacles traveling along the vehicle’s path, and present
preliminary results extending the technique to the scenario of
obstacles crossing the vehicle’s path.
Clothing 17:50
|