BibTex format
@unpublished{Ortiz:2020,
author = {Ortiz, J and Pupilli, M and Leutenegger, S and Davison, AJ},
publisher = {arXiv},
title = {Bundle adjustment on a graph processor},
url = {http://arxiv.org/abs/2003.03134v2},
year = {2020}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - UNPB
AB - Graph processors such as Graphcore's Intelligence Processing Unit (IPU) arepart of the major new wave of novel computer architecture for AI, and have ageneral design with massively parallel computation, distributed on-chip memoryand very high inter-core communication bandwidth which allows breakthroughperformance for message passing algorithms on arbitrary graphs. We show for thefirst time that the classical computer vision problem of bundle adjustment (BA)can be solved extremely fast on a graph processor using Gaussian BeliefPropagation. Our simple but fully parallel implementation uses the 1216 coreson a single IPU chip to, for instance, solve a real BA problem with 125keyframes and 1919 points in under 40ms, compared to 1450ms for the Ceres CPUlibrary. Further code optimisation will surely increase this difference onstatic problems, but we argue that the real promise of graph processing is forflexible in-place optimisation of general, dynamically changing factor graphsrepresenting Spatial AI problems. We give indications of this with experimentsshowing the ability of GBP to efficiently solve incremental SLAM problems, anddeal with robust cost functions and different types of factors.
AU - Ortiz,J
AU - Pupilli,M
AU - Leutenegger,S
AU - Davison,AJ
PB - arXiv
PY - 2020///
TI - Bundle adjustment on a graph processor
UR - http://arxiv.org/abs/2003.03134v2
UR - http://hdl.handle.net/10044/1/79819
ER -