Estimation of Absolute Scale in Monocular SLAM Using Synthetic Data

Danila Rukhovich, Daniel Mouritzen, Ralf Kaestner, Martin Rufli, Alexander Velizhev International Conference on Computer Vision (ICCV) 2019 – Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving This paper addresses the problem of scale estimation in monocular SLAM by estimating absolute distances between camera centers of consecutive image frames. These estimates would improve the overall performance of classical(not deep) SLAM systems and allow metric feature locations to be recovered from a single monocular camera. We propose several network architectures that lead to an improvement of scale estimation accuracy over the state of the art. In addition, we exploit...

Deliverable 9.6

Press video This deliverable provides the UP-Drive press video. The video itself is placed at the landing page of the project webpage at – it is the bottom video. pdf

Deliverable 5.2

First development and integration cycle of lifelong mapping This deliverable describes the lifelong mapping framework after the first development & integration cycle. All components, notably the metric and semantic map, the metric online localization, the semantic data aggregation and the map summarization are functional and integrated on the vehicles, fulfill their basic purposes and interact with each other in a limited fashion. All components deliver first evaluation results. 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 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

A Decentralized Trust-minimized Cloud Robotics Architecture

Alessandro Simovic, Ralf Kaestner and Martin Rufli International Conference on Intelligent Robots and Systems (IROS) 2017 – Poster Track We introduce a novel, decentralized architecture facilitating consensual, blockchain-secured computation and verification of data/knowledge. Through the integration of (i) a decentralized content-addressable storage system, (ii) a decentralized communication and time stamping server, and (iii) a decentralized computation module, it enables a scalable, transparent, and semantically interoperable cloud robotics ecosystem, capable of powering the emerging internet of robots. Paper (.pdf)   Poster (.pdf)