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November 8 – 13 , 2015, Dagstuhl Seminar 15461

Vision for Autonomous Vehicles and Probes

Organizers

Andrés Bruhn (Universität Stuttgart, DE)
Atsushi Imiya (Chiba University, JP)
Ales Leonardis (University of Birmingham, GB)
Tomas Pajdla (Czech Technical University, CZ)

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Documents

Dagstuhl Report, Volume 5, Issue 11 Dagstuhl Report
Aims & Scope
List of Participants
Shared Documents
Dagstuhl Seminar Wiki
Dagstuhl Seminar Schedule [pdf]

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Summary

Computer vision plays a key role in advanced driver assistance systems (ADAS) as well as in exploratory and service robotics. Visual odometry, trajectory planning for Mars exploratory rovers and the recognition of scientific targets in images are examples of successful applications. In addition, new computer vision theory focuses on supporting autonomous driving and navigation as applications to unmanned aerial vehicles (UAVs) and underwater robots. From the viewpoint of geometrical methods for autonomous driving, navigation and exploration, the on-board calibration of multiple cameras, simultaneous localisation and mapping (SLAM) in non-human-made environments and the processing of non-classical features are some of current problems. Furthermore, the adaptation of algorithms to long image sequences, image pairs with large displacements and image sequences with changing illumination is desired for robust navigation and exploration. Moreover, the extraction of non-verbal and graphical information from environments to remote driver assistance is required.

Based on these wide range of theoretical interests from computer vision for new possibility of practical applications of computer vision and robotics, 38 participants (excluding organisers) attended from variety of countries: 4 from Australia, 3 from Austria, 3 from Canada, 1 from Denmark, 11 from Germany, 1 from Greece, 1 from France, 3 from Japan, 4 from Spain, 2 from Sweden, 4 from Switzerland and 3 from the US.

The seminar was workshop style. The talks are 40 mins and 30 mins for young researchers and for presenters in special sessions. The talks have been separated into sessions on aerial vehicle vision, under water and space vision, map building, three-dimensional scene and motion understanding as well as a dedicated session on robotics. In these tasks, various types of autonomous systems such as autonomous aerial vehicles, under water robots, field and space probes for remote exploration and autonomous driving cars were presented. Moreover, applications of state-of-the-art computer vision techniques such as global optimization methods, deep learning approaches as well as geometrical methods for scene reconstruction and understanding were discussed. Finally, with Seminar 15462 a joint session on autonomous driving with leading experts in the field was organised.

The working groups are focused on "Sensing", "Interpretation and Map building", and "Deep leaning". Sensing requires fundamental methodologies in computer vision. Low-level sensing is a traditional problem in computer vision. For applications of computer-vision algorithms to autonomous vehicles and probes, reformulation of problems for various conditions are required. Map building is a growing area including applications to autonomous robotics and urban computer vision. Today, application to autonomous map generation involves classical SLAM and large-scale reconstruction from indoor to urban sizes. Furthermore, for SLAM on-board and on–line computation is required. Deep learning, which goes back its origin to '70s, is a fundamental tool for image pattern recognition and classification. Although the method showed significant progress in image pattern recognition and discrimination, for applications to spatial recognition and three-dimensional scene understanding, we need detailed discussion and developments.

Through talks-and-discussion and working-group discussion, the seminar clarified that for designing of platforms for visual interpretation and understanding of three-dimensional world around the system, machine vision provides fundamental and essential methodologies. There is the other methodology which uses computer vision as a sensing system for the acquisition of geometrical data and analysis of motion around cars. For these visual servo systems, computer vision is a part of the platform for intelligent visual servo system. The former methodology is a promising one to provide a fundamental platform which is common to both autonomous vehicles, which are desired for consumer intelligence, and probes, which are used for remote exploration.

License
  Creative Commons BY 3.0 Unported license
  Andrés Bruhn and Atsushi Imiya

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