activity
20142026
most citedIntroducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

113 citations · 167 across the 5 of their papers we have counts for

collaborators

5 papers

cs.ET2026

The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

Stefan Scholze, Johannes Partzsch, Sebastian Höppner +27

In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an…

cs.NE2026

Heterogeneous computing platform for real-time robotics

Jakub Fil, Yulia Sandamirskaya, Hector Gonzalez +18

After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient a…

cs.PF2025★ 3 cited

Constructive community race: full-density spiking neural network model drives neuromorphic computing

Johanna Senk, Anno C. Kurth, Steve Furber +18

The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a mo…

cs.CV2018★ 51 cited

Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

Sajad Saeedi, Bruno Bodin, Harry Wagstaff +23

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is cent…

cs.RO2014★ 113 cited

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Luigi Nardi, Bruno Bodin, M. Zeeshan Zia +9

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their hi…