113 citations · 230 across the 7 of their papers we have counts for
7 papers
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Bruno Bodin, Harry Wagstaff, Sajad Saeedi +9
SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify…
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…
Project Beehive: A Hardware/Software Co-designed Stack for Runtime and Architectural Research
Christos Kotselidis, Andrey Rodchenko, Colin Barrett +14
The end of Dennard scaling combined with stagnation in architectural and compiler optimizations makes it challenging to achieve significant performance deltas. Solutions based sole…
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…
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
Matthew Crossley, Andy Nisbet, Martyn Amos
The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination o…
Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
Matthew Crossley, Andy Nisbet, Martyn Amos
A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific meth…
Parallelization Strategies for Ant Colony Optimisation on GPUs
Jose M. Cecilia, Jose M. Garcia, Manuel Ujaldon +2
Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is in…