Deliverable 9.1

Project Web-page

This deliverable corresponds to Task 9.2: Set-up of fully functional project web site. In the current stage the web site offers detailed information about the project and of the partners in UP-Drive consortium. The web site will be updated continuously to communicate to the public any project related news.

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Deliverable 6.1

Software specification and architecture for scene understanding

This deliverable contributes to the UP-Drive (automated Urban Parking) project’s endeavor to create a car capable of self-driving in an unconstrained urban environment with speeds up to 30 km/h.

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Deliverable 5.1

Specification of the Map Frontend and Storage Concept

This deliverable corresponds to task 5.1, 5.2 and 5.3. It describes the hardware and software requirements and specifications for the mapping and localization frontend and storage concepts in the cloud-based backend.

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Deliverable 4.1

Initial specification and design of on-board sensing

This deliverable states the sensing possibilities, suitable to enable vehicle’s highly automated driving capabilities, as well as to collect useful information for map related operations including map enrichment, alignment, etc.

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Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jose Neira, Ian Reid and John J. Leonard

IEEE Transactions on Robotics 32 (6) pp 1309-1332, 2016

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors’ take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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@article{Cadena16tro-SLAMfuture,
 title = {Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age},
 author = {C. Cadena and L. Carlone and H. Carrillo and Y. Latif and D. Scaramuzza and J. Neira and I. Reid and J.J. Leonard},
 journal = {{IEEE Transactions on Robotics}},
 year = {2016},
 number = {6},
 pages  = {1309--1332},
 volume = {32},
}

Appearance-Based Landmark Selection for Efficient Long-Term Visual Localization

Mathias Buerki, Igor Gilitschenski, Elena Stumm, Roland Siegwart, and Juan Nieto

International Conference on Intelligent Robots and Systems (IROS) 2016

landmark_selectionWe present an online landmark selection method for efficient and accurate visual localization under changing appearance conditions. The wide range of conditions encountered during long-term visual localization by e.g. fleets of autonomous vehicles offers the potential exploit redundancy and reduce data usage by selecting only those visual cues which are relevant at the given time. Therefore co-observability statistics guide landmark ranking and selection, significantly reducing the amount of information used for localization while maintaining or even improving accuracy.

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@inproceedings{BuerkiIROS2016,
Title = {Appearance-Based Landmark Selection for Efficient Long-Term Visual Localization},
Author = {M. Buerki and I. Gilitschenski and E. Stumm and R. Siegwart and J. Nieto},
Fullauthor = {Mathias Buerki and Igor Gilitschenski and Elena Stumm and Roland Siegwart and Juan Nieto},
Booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
Address = {Daejeon, Korea},
Month = {October},
Year = {2016},
}