Comparing view-based and map-based semantic labelling in real-time SLAM
File(s)
Working paper
Author(s)
Landgraf, Zoe
Falck, Fabian
Bloesch, Michael
Leutenegger, Stefan
Davison, Andrew
Type
Working Paper
Abstract
Generally capable Spatial AI systems must build persistent scene
representations where geometric models are combined with meaningful semantic
labels. The many approaches to labelling scenes can be divided into two clear
groups: view-based which estimate labels from the input view-wise data and then
incrementally fuse them into the scene model as it is built; and map-based
which label the generated scene model. However, there has so far been no
attempt to quantitatively compare view-based and map-based labelling. Here, we
present an experimental framework and comparison which uses real-time height
map fusion as an accessible platform for a fair comparison, opening up the
route to further systematic research in this area.
representations where geometric models are combined with meaningful semantic
labels. The many approaches to labelling scenes can be divided into two clear
groups: view-based which estimate labels from the input view-wise data and then
incrementally fuse them into the scene model as it is built; and map-based
which label the generated scene model. However, there has so far been no
attempt to quantitatively compare view-based and map-based labelling. Here, we
present an experimental framework and comparison which uses real-time height
map fusion as an accessible platform for a fair comparison, opening up the
route to further systematic research in this area.
Date Issued
2020-02-24
Citation
2020
Publisher
arXiv
Copyright Statement
© 2020 The Author(s)
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Dyson Technology Limited
Identifier
http://arxiv.org/abs/2002.10342v1
Grant Number
EP/S036636/1
PO4500503359
Subjects
cs.CV
cs.CV
Notes
ICRA 2020
Publication Status
Published