2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles

Full Day Workshop in Room Fenix 2

October 7th, 2012 Vilamoura, Algarve, Portugal

Workshop Proceedings, Program

Contact : Professor Philippe Martinet
IRCCYN-CNRS Laboratory, Ecole Centrale de Nantes,
1 rue de la Noë
44321 Nantes Cedex 03, France
Phone: +33 237406975, Fax: +33 237406934,
Email: Philippe.Martinet@irccyn.ec-nantes.fr,
Home page: http://www.irccyn.ec-nantes.fr/~martinet



Organizers

Research Director Christian Laugier, INRIA, Emotion project, INRIA Rhône-Alpes, 655 Avenue de l'Europe, 38334 Saint Ismier Cedex, France, Phone: +33 4 7661 5222, Fax : +33 4 7661 5477, Email: Christian.Laugier@inrialpes.fr,
Home page: http://emotion.inrialpes.fr/laugier

Professor Philippe Martinet, IRCCYN-CNRS Laboratory, Ecole Centrale de Nantes, 1 rue de la Noë, 44321 Nantes Cedex 03, France, Phone: +33 237406975, Fax: +33 237406934, Email: Philippe.Martinet@irccyn.ec-nantes.fr,
Home page: http://www.irccyn.ec-nantes.fr/~martinet

Professor Urbano Nunes, Department of Electrical and Computer Engineering of the Faculty of Sciences and Technology of University of Coimbra, 3030-290 Coimbra, Portugal, GABINETE 3A.10, Phone: +351 239 796 287, Fax: +351 239 406 672, Email: urbano@deec.uc.pt,
Home page: http://www.isr.uc.pt/~urbano

Professor Christoph Stiller, , Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie (KIT), Engler-Bunte-Ring 21, Gebäude: 40.32, 76131 Karlsruhe, Germany, Phone: +49 721 608-42325 Fax: +49 721 661874, Email: stiller@kit edu
Home page: http://www.mrt.kit.edu/mitarbeiter_stiller.php

Professor Alberto Broggi, Dipartimento di Ingegneria dell'Informazione,Università di Parma, Parco Area delle Scienze 181A, I-43100 Parma, Italy Phone: +39 0521 905707 Fax: +39 0521 905723, Email: broggi@ce.unipr.it
Home page: http://www.ce.unipr.it/~broggi/

General Scope

Autonomous driving and navigation is a major research issue which would affect our lives in near future. The purpose of this workshop is to discuss topics related to the challenging problems of autonomous navigation and of driving assistance in open and dynamic environments. Technologies related to application fields such as unmanned outdoor vehicles or intelligent road vehicles will be considered from both the theoretical and technological point of views. Several research questions located on the cutting edge of the state of the art will be addressed. Among the many application areas that robotics is addressing, transportation of people and goods seem to be a domain that will dramatically benefit from intelligent automation. Fully automatic driving is emerging as the approach to dramatically improve efficiency while at the same time leading to the goal of zero fatalities. Theses new technologies can be applied efficiently for other application field such as unmanned vehicles, mobile service robots, or mobile devices for motion assistance to elderly or disable peoples. Technologies related to this area, such as autonomous outdoor vehicles, achievements, challenges and open questions would be presented.

Main Topics

  • Road scene understanding
  • Lane detection and lane keeping
  • Pedestrian and vehicle detection
  • Detection, tracking and classification
  • Feature extraction and feature selection
  • Cooperative techniques
  • Collision prediction and avoidance
  • Driver assistance systems
  • Environment perception, vehicle localization and autonomous navigation
  • Real-time perception and sensor fusion
  • SLAM in dynamic environments
  • Real-time motion planning in dynamic environments
  • 3D Modelling and reconstruction
  • Human-Robot Interaction
  • Behavior modeling and learning
  • Robust sensor-based 3D reconstruction
  • Modeling and Control of mobile robot
  • Multi-agent based architectures
  • Cooperative unmanned vehicles (not restricted to ground transportation)
  • Multi autonomous vehicles studies, models,techniques and simulations
  • International Program Committee

  • Alberto Broggi (VisLab, Parma University, Italy)
  • Javier Ibanez-Guzman (Renault, France)
  • Zhencheng Hu, (Kumamoto University, Japan)
  • Christian Laugier (Emotion, INRIA, France)
  • Philippe Martinet (Blaise Pascal University, France)
  • Urbano Nunes (Coimbra University, Portugal),
  • Anya Petrovskaya (Stanford University, USA)
  • Cedric Pradalier, (ETH Zurich, Switzerland)
  • Roland Siegwart, (ETH Zurich, Switzerland)
  • Cyrill Stachniss, (University of Freiburg, Germany)
  • Christoph Stiller (Karlsruhe Institute of Technology, Germany)
  • Sebastian Thrun (Stanford University, USA)
  • Ming Yang, (SJTU Shanghai, China)
  • Final program

    Introduction to the workshop 8:25

    Session I: Localization & mapping 8:30
    Chairman: C. Laugier (INRIA, France)

    • Title: Navigable Maps for Intelligent Vehicles Localization and Perception 8:30
      Keynote speaker: Philippe Bonnifait (UTC, Compiegne, France) 35min + 5min questions
      Presentation

      Abstract: Intelligent Vehicles are robotic systems that assist the driver in safe and comfortable operation by providing pertinent information or by controlling the vehicle itself. Real-time and safe perception of the driving environment is one of the key issues. Recent evolutions of navigable maps make them suitable to assist localization and perception processes since they provide additional information that can be exploited with anticipation. This talk focuses on some autonomous techniques that merge the map information with on-board sensors data like GPS measurements, CAN -bus proprioceptive sensors, exteroceptive cameras and multi-layers lidars. Macro-scale maps with poly-lines representation of the road network can be exploited as an a priori knowledge in order to enhance GPS availability, particularly in urban canyons where satellites signals are often blocked. Such kind of map technology is also planned to be used for Map-aided ADAS (Advanced Driver Assistance Systems). However, maps can be obsolete or contain errors, resulting in malfunctions of context-based ADAS and possibly generating hazardous situations. The talk will present a sequential fault detection test able to detect and localise map errors in an autonomous manner using the on-board sensors. Meso-scale maps provide more refined information that describes the drivable space of the roads. The talk will present how 3-D facets geometry can be used for contracting East, North and altitude estimates when solving a localization problem. The use of this kind of 3D representation to characterize the drivable space (useful for path planning or obstacle avoidance) will be also presented and discussed. Finally, the talk will focus on visual landmarks that can be managed in a specific layer of the map. A method for mobile mapping lane markings and exploiting them in dynamic localization will be described. Experimental results showing the key role of navigable maps for intelligent vehicles localization and perception will be systematically presented.

    • Title: Robot Localization using efficient planar features matching 9:10
      Authors: B. Charmette, E. Royer, Frédéric Chausse and L. Lequievre 17min + 3min questions
      Paper, Presentation, Video1

      Abstract: Real-time accurate localization is a key component of an autonomous mobile robot. Visual localization algorithms usually rely on feature matching between the current view and a map using point descriptors. Many descriptors such as SIFT or SURF are designed to recognize features seen from different viewpoint. But in a robotic context, the robot movement can be modeled and bring useful information for the matching problem. In this paper we detail a way of matching features with a local 3D model of the features taking advantage of the motion model of the robot. We describe then methods to describe the motion model. The experimental results show how useful the motion model of robot movement is, and prove that use of other sensors can greatly improve precision and robustness of the localization.

    • Title: Application of Visual-Inertial SLAM for 3D Mapping of Underground Environments 9:30
      Authors: A. Ferreira, J. Almeida and E. Silva 17min + 3min questions
      Paper, Presentation

      Abstract:The underground scenarios are one of the most challenging environments for accurate and precise 3d mapping where hostile conditions like absence of Global Positioning Systems, extreme lighting variations and geometrically smooth surfaces may be expected. So far, the state-of-the-art methods in underground modelling remain restricted to environments in which pronounced geometric features are abundant. This limitation is a consequence of the scan matching algorithms used to solve the localization and registration problems. This paper contributes to the expansion of the modelling capabilities to structures characterized by uniform geometry and smooth surfaces, as is the case of road and train tunnels. To achieve that, we combine some state of the art techniques from mobile robotics, and propose a method for 6DOF platform positioning in such scenarios, that is latter used for the environment modelling. A visual monocular Simultaneous Localization and Mapping (MonoSLAM) approach based on the Extended Kalman Filter (EKF), complemented by the introduction of inertial measurements in the prediction step, allows our system to localize himself over long distances, using exclusively sensors carried on board a mobile platform. By feeding the Extended Kalman Filter with inertial data we were able to overcome the major problem related with MonoSLAM implementations, known as scale factor ambiguity. Despite extreme lighting variations, reliable visual features were extracted through the SIFT algorithm, and inserted directly in the EKF mechanism according to the Inverse Depth Parametrization. Through the 1-Point RANSAC (Random Sample Consensus) wrong frame-to-frame feature matches were rejected. The developed method was tested based on a dataset acquired inside a road tunnel and the navigation results compared with a ground truth obtained by post-processing a high grade Inertial Navigation System and L1/L2 RTK-GPS measurements acquired outside the tunnel. Results from the localization strategy are presented and analyzed.

    Session II: Multiple Vehicles/Robots & Interaction 9:50
    Chairman: P. Bonnifait (Heudiasyc, France)
    • Title: Thoughts on perception for intelligent vehicles 9:50
      Keynote speaker: Alberto Broggi (Parma University, Parma, Italy) 35min + 5min questions
      Co-Authors: P. Grisleri and P. Zani
      Presentation

      Abstract: Many successful implementations of intelligent vehicles are using laser based technology to obtain a 360 degrees view of the area surrounding the vehicle. Such technology is able to provide very dense 3D point clouds covering an extended range. The talk will compare this technology to other lower cost alternatives, such as vision, and discuss some possible implementations.

    • Coffee Break 10:30 ==> 11:00

    • Title: Multiple Robots in a Cooperating Task: Exploration and Mapping 11:00
      Authors: A.M. Neto, P. Rosa, T.E. Alves de Oliveira and P.C. Pellanda 17min + 3min questions
      Paper, Presentation

      Abstract: This work presents an exploration task with multiple vehicles using occupancy grids and a technique of simultaneous localization and mapping (SLAM). The exploration strategy uses concepts of costs and utility from frontier-cells. Besides, the used SLAM method is based on a FastSLAM algorithm with landmarks extracted from visual sensors and a features map common to two vehicles. Both activities - location of the vehicles and exploration of the environment - are coordinated by a central agent. The results show that when two vehicles can communicate with a central agent building a features map common to the vehicles, the exploration task becomes more efficient than when performed with dedicated maps, because the accuracy of vehicle position and orientation is increased with the use of an even number of particles. In this paper we also present and evaluate the implementation of the approach in a real environment.

    • Title: Dynamic Obstacle Avoidance Strategies using Limit Cycle for the Navigation of Multi-Robot System 11:20
      Authors: A. Benzerrouk, L. Adouane and P. Martinet 17min + 3min questions
      Paper, Presentation, Video1, Video2

      Abstract: This paper deals with the navigation of a multirobot system (MRS). The latter must reach and maintain a specific formation in dynamic environment. In such areas, the collision avoidance between the robots themselves and with other obstacles (static and dynamic) is a challenging issue. To deal with it, a reactive and a distributed control architecture is proposed. The navigation in formation of the MRS is insured while tracking a global virtual structure. In addition, according to the robots’ perception context (e.g., static or dynamic obstacle), the most suitable obstacle avoidance strategy is activated. These approaches use mainly the limitcycle principle and a penalty function to obtain linear and angular robots’ velocities. The proposed control law guarantees the stability (using Lyapunov function) and the safety of the MRS. The robustness and the efficiency of the proposed control architecture is demonstrated through a multitude of experiments which shows the MRS in different configuration of avoidance.

    Lunch break 12:00

    Session III: Interactive session 13:30
    Chairman: P. Martinet (IRCCYN/ECN, Nantes, France)
    • Title: Development of an Autonomous Vehicle for High-speed Navigation and Obstacle Avoidance
      Authors: J.H. Ryu, D. Ogay, S. Bulavintsev, H. Kim, and J.S.Park
      Paper

      Abstract: This paper introduces the autonomous vehicle Pharos, which participated in the 2010 Autonomous Vehicle Competition organized by Hyundai-Kia motors. Pharos was developed for high-speed on/off-road unmanned driving avoid- ing diverse patterns of obstacles. For the high speed traveling up to 60 Km/h, long range terrain perception, real-time path planning and high speed vehicle motion control algorithms are developed. This paper describes the major hardware and software components of our vehicle.

    • Title: Kinodynamic motion planning with state Lattice Motion Primitives
      Authors: M. Pivoraiko and A. Kelly
      Paper

      Abstract: This paper presents a type of motion primitives than can be used for building efficient kinodynamic motion planners. The primitives are pre-computed to meet two obectives: to capture the mobility constraints of the robot as well as possible and to establish a state sampling policy that is conducive to efficent search. The first objective allows encoding mobility constraints into primitives, thereby enabling fast unconstrained search to produce feasible solutions. The second objective enables high quality (lattice) sampling of state space, further speeding up exploration during search. We further discuss several novel results enabled by using such motion primitives for kinodynamic planning, including incremental search, efficient bi-directional search and incremental sampling.

    • Title: Detection of Moving and Stationary Objects at High Velocities using Cost-Efficient Sensors, Curve-Fitting and Neural Networks
      Authors: F. Mirus, J. Pfadt, C. Connette, B. Ewert, D. Grudl, A. Verl
      Paper, Poster

      Abstract: In recent years, driver-assistance systems have emerged as one major possibility to increase comfort and – even more important – safety in road traffic. Still, cost is one major hindrance to the widespread use of safety systems such as lane change or blind spot warning. To facilitate the widespread adoption of such assistance systems, thus increasing safety for all traffic participants, the use of cost-efficient components is of crucial importance. This paper investigates the usage of cost-efficient, widely used ultrasonic sensors for blind spot warning at high velocities. After discussing the requirements and setup of such a system a model-based approach for the detection of moving and stationary objects is outlined. The sensor-signal is compared with a precalculated curve data base and the correlation-coefficients are feeded into a neural network. To revise its performance the concept at hand is qualitatively and quantitatively evaluated in real road traffic situations under different driving conditions.

    • Title: ESTRO: Design and Development of Intelligent Autonomous Vehicle for Shuttle Service in the ETRI
      Authors: J. Byun, K.I. Na, M. Noh and S. Kim
      Paper, Poster

      Abstract: ESTRO(ETRI Smart Transport RObot) Project aims at the development of autonomous vehicle to transport goods and people without the help of driver within the well-structured section such campus and premises without traffic regulations. To do so, we have designed and implemented the autonomous vehicle modified electronic vehicle. In addition to we have constraints that have to minimize the cost of sensor and optimize the complexity of system unlike autonomous vehicles introduced in recent years for driving in urban traffic scenarios. This paper proposes the design of H/W and S/W architecture for the autonomous vehicle and describes the method of environmental perception and navigation. The implemented system is currently has been operational test in our institute campus.

    • Title: An effective 6DoF motion model for 3D-6DoF Monte Carlo Localization
      Authors: A. L. Ballardini, A. Furlan, A. Galbiati, M. Matteucci, F. Sacchi, D. G. Sorrenti
      Paper

      Abstract: This paper deals with the probabilistic 6DoF motion model of a wheeled road vehicle. It allows to correctly model the error introduced by dead reckoning. Furthermore, to stress the importance of an appropriate motion model, i.e., that different models are not equally good, we show that another model, which was previously developed, does not allow a correct representation of the uncertainty, therefore misguiding 3D- 6DoF Monte Carlo Localization. We also present experiments, in simulated settings as well as on field, to demonstrate that our model allow a consistent determination of the 6DoF vehicle pose.

    • Title: Visual trajectory learning and following in unknown routes for autonomous navigation
      Authors: D. A. Marquez-Gamez and M. Devy
      Paper

      Abstract: This paper describes the design and testing of a system to enable large scale cooperative navigation of autonomous vehicles moving on a priori unknown routes. A large-scale learning-mapping approach and a map-based replay-localization method are combined to achieve cooperative navigation. The mapping approach is based on a proposed hierarchical/ hybrid BiCam SLAM approach -global level and local maps-, which will be generalized to be executed on multiple vehicles moving as a convoy. A global 3D map maintains the relationships between a series of local maps built by the first vehicle of the convoy (leader), defining a path that all other vehicles (followers) must stay on. Only single camera setups are considered. The overall approach is evaluated with real data acquired in an urban environment.

    • Title: Eigen analysis and gray alignment for shadow detection applied to urban scene images
      Authors: T. Souza, L. Schnitman and L. Oliveira
      Paper

      Abstract: Urban scene analysis is very useful for many intelligent transportation systems (ITS), such as license plate detection, pedestrian detection, video surveillance, and so forth. In fact, all these systems are prone to noisy of any kind, which harms their performance. Among noisy factors, shadow may represent a critical line between the success or fail within an ITS framework. Shadow detection usually provides benefits for further stages of machine vision systems, and its application will depend on the computational load of the detection system. To cope with all these situations, a novel shadow detection method applied to urban scenes is proposed here. This method is based on a measure of the energy defined by the summation of the eigenvalues of image patches. The final decision of an image region to contain a shadow is made according to a new metric for unsupervised classification called here as a gray alignment. The characteristics of the proposed method include no supervision, very low computational cost and mathematical background unification, which turns the method effective. Our proposed approach was evaluated on a public dataset, demonstrating state-of-the-art performance.

    Session IV: Navigation, Control, Planning 14:30
    Chairman: P. Grisleri (Parma University, Parma, Italy)
    • Title: Interpretation of Situative Sensor-Data and Continuous Decision Making for Cognitive Automobiles 14:30
      Keynote speaker:Rüdiger Dillmann (KIT, Karlsruhe, Germany) 35min + 5min questions
      Co-Authors: S. Brechtel, T. Gindele
      Presentation

      Abstract: Autonomous automobiles must be capable to decide continuously on their adequate behaviour in a highly dynamic environment. Usually such vehicles are supported with uncomplete and noisy perceptive data with different sensor modalities. Probabilistic and predictive methods can be applied to estimate the actual situationand the intension of other traffic agents around the autonomous vehicle and to generate the best and safe behaviour. Predictive driving requires understanding the sensorial observations and the behaviour of the other vehicles in standard and non standard traffic situations. With such a background, structural bootstrapping towards not befor seen situations can be realized and logically explained with the help of learning strategies. A probate method is to collect automatically experience from traing data and online observations in form of Bayesian Nets. For processing of the consequences of actions under uncertainties continuous POMDPs may be suitable to generate adequate decisions. Planning algorithms, decision problems and its implementation and experimental verification as well as actual research results are presented in this talk.

    • Title: An Efficient Heuristic Estimate for Non-holonomic Motion Planning 15:10
      Authors: J.W. Choi 17min + 3min questions
      Paper, Presentation

      Abstract: A new efficient and admissible heuristic estimate function is proposed for non-holonomic motion planning. The heuristic calculation begins by separating relatively open local area around the goal from others. Then pre-computed heuristics in obstacle-free full state space are assigned to the area. The heuristic for the other area is obtained by using dynamic programming to extende the full state heuristic in reduced 2D state space. The numerical simulations demonstrate remarkable performance improvement by applying the heuristic function, compared to other existing heuristics.

    • Title: Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition 15:30
      Authors: M. Oliveira, V. Santos and A.D. Sappa 17min + 3min questions
      Paper, Presentation, Video1, Video2, Video3, Video4

      Abstract: This paper describes a practical implementation of short term path planning in autonomous navigation in unmapped or unstructured environments. Path planning is performed by generating multiple hypothesis of paths for the robot and then evaluating the quality of each path. In very dynamic environments, long term path planning is generally not very useful, so this paper embraces the approach of short term path planning and continuously revises the path plan and motion parameters after the perception from its onboard sensors. The solution has been applied to small scale robots that compete in an autonomous driving competition. These robots have won the last six editions of this competition.

    Coffee break 16:00

    Session V: Perception & Situation awareness 16:30
    Chairman: C. Laugier (INRIA, Grenoble, France)
    • Title: Real-time Scan-Matching Using L0-norm Minimization Under Dynamic Crowded Environment 16:30
      Authors: Y. Hieida, T. Suenaga, K. Takemura, J. Takamatsu and T. Ogasawara 17min + 3min questions
      Paper, Presentation

      Abstract: We propose real-time scan-matching based on L0- norm minimization under dynamic crowded environment. The prior scan-matching methods are based on L2-norm minimization, because the measurement noise follows the normal distribution in static environments. This assumption is unfortunately broken in dynamic crowded environments. We propose to use the idea of Locality Sensitive Hashing (LSH) to accelerate the L0-norm minimization, which usually is a time-consuming process. The LSH customized for our issue reduces the calculation time even in the worst cases. The experimental results demonstrate the effectiveness of the proposed method compared with standard L2-norm minimization and its robust version with M-estimator.

    • Title: Fast classification of static and dynamic environment for Bayesian Occupancy Filter (BOF) 16:50
      Authors: Q. Baig, M. Perrollaz, J. Botelho Do Nascimento, C. Laugier 17min + 3min questions
      Paper, Presentation, Video1

      Abstract: In this paper we present a fast motion detection technique based on laser data and odometry/imu information. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between two consecutive data grids. We plan to use the output of this work for Bayesian Occupancy Filter (BOF) framework to reduce processing time and improve the results of subsequent clustering and tracking algorithm, based on BOF. Experimental results obtained from a real demonstrator vehicle show the effectiveness of our technique.

    • Title: Localization and Mapping in Dynamic and Changing Environments 17:10
      Keynote speaker: Wolfram Burgard (Freiburg University, Frieburg, Germany) 35min + 5min questions
      Co-Authors: Gian Diego Tipaldi
      Paper, Presentation, Video1, Video2, Video3, Video4, Video5, Video6, Video7

      Abstract: The majority of existing approaches to mobile robot localization and mapping assumes that the world is static, ignoring the dynamics inherent in most real world scenarios like parking lots, warehouses and even offices and households. In such environments the configuration of certain objects such as cars, goods, or furniture can change with time leading to inconsistent observations with respect to previously learned maps and thus decreasing the localization accuracy. In this talk we present a probabilistic grid-based approach for modeling changing environments. Our method represents both, the occupancy and its changes in the corresponding area where the dynamics are characterized by the state transition probabilities of a Hidden Markov Model. We further present a novel probabilistic approach to lifelong localization in changing environments, where the robot pose and the environment state are jointly estimated using a Rao-Blackwellized particle filter. Exploiting several characteristics of HMMs, we can considerably speed up the estimation procedure. This makes it feasible to run our algorithm on-line. Experimental results obtained with data acquired by real robots demonstrate that our model is well-suited for representing changing environments. Further results demonstrate that our approach can reliably adapt to changes in the environment and that it significantly improves standard localization techniques.

    Closing 17:50
    Author Information

      Format of the paper: Papers should be prepared according to the IROS12 final camera ready format and should be 4 to 6 pages long. The detailed information on the paper format is available from the IROS12 page. http://www.iros2012.org/site/node/21. Papers must be sent to Philippe Martinet by email at Philippe.Martinet@irccyn.ec-nantes.fr

      Important dates

      • Deadline for Paper submission: June 30th (extended to july 5th), 2012
      • Acceptance with review comments: July 15th (delayed to july 29th), 2012
      • Deadline for final paper submission: August 20th, 12am at last, 2012

      Talk information

      • Invited talk: 40 min (35 min talk, 5 min question)
      • Other talk: 20 min (17 min talk, 3 min question)

      Interactive session

      • Interactive and open session: 1h00

    Previous workshops

      Previously, seven workshops were organized in the near same field. The 1st edition PPNIV'07 of this workshop was held in Roma during ICRA'07 (around 60 attendees), the second PPNIV'08 was in Nice during IROS'08 (more than 90 registered people), and the third edition PPNIV'09 was in Saint-Louis (around 70 attendees) during IROS'09 . In parallel, we have also organized SNODE'07 in San Diego during IROS'07 (around 80 attendees), SNODE'09 in Kobe during ICRA'09 (around 70 attendees), and RITS'10 in Anchrorage during ICRA'10 (around 35 attendees), and the last one PNAVHE11 in San Francisco during the last IROS11(around 50 attendees).

      A special issue in IEEE Transaction on ITS, mainly focused on Car and ITS applications, has been published in September 2009. Currently there is call for contribution for a special issue in IEEE RAS magazine IEEE Robotics and Automation Magazine Special issue on Perception and Navigation for Autonomous Vehicles . We are preparing one proposal for a special issue in International Journal of Robotic Research Research. Best papers will be pushed to be extended and submitted to these special issues.

    Keynotes

      Proceedings: The workshop proceedings will be published within the IROS Workshop/Tutorial CDROM and electronically as a pdf file.

      Special issue: Selected papers will be considered for a special issue in an International Journal in connection with this workshop. We will issue an open call after the workshop, submissions will go through a separate peer review process.