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. pdf

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. pdf

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. pdf

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. pdf

Deliverable 3.2

First development and integration cycle of cloud infrastructure This deliverable corresponds to Task 3.2: First development and integration cycle of cloud infrastructure. It documents the cloud infrastructure that has been selected and implemented within the project. Building on D3.1, this deliverable focuses on the Gitlab source code management system, the Swift object store and OpenStack cloud compute infrastructure functionality. pdf

Deliverable 2.1

First vehicle platform available This deliverable documents the availability of the first vehicle platform. It gives an overview of the available sensor data, drive by wire functionality and the low-level functionality of the communication, acquisition and processing framework. pdf

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...