Fusion++: Volumetric object-level SLAM

File Description SizeFormat 
1808.08378.pdfAccepted version9.71 MBAdobe PDFView/Open
Title: Fusion++: Volumetric object-level SLAM
Authors: McCormac, J
Clark, R
Bloesch, M
Davison, A
Leutenegger, S
Item Type: Conference Paper
Abstract: We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions with object size-dependent resolutions and a novel 3D foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation. Objects are incrementally refined via depth fusion, and are used for tracking, relocalisation and loop closure detection. Loop closures cause adjustments in the relative pose estimates of object instances, but no intra-object warping. Each object also carries semantic information which is refined over time and an existence probability to account for spurious instance predictions. We demonstrate our approach on a hand-held RGB-D sequence from a cluttered office scene with a large number and variety of object instances, highlighting how the system closes loops and makes good use of existing objects on repeated loops. We quantitatively evaluate the trajectory error of our system against a baseline approach on the RGB-D SLAM benchmark, and qualitatively compare reconstruction quality of discovered objects on the YCB video dataset. Performance evaluation shows our approach is highly memory efficient and runs online at 4-8Hz (excluding relocalisation) despite not being optimised at the software level.
Issue Date: 15-Oct-2018
Date of Acceptance: 5-Sep-2018
URI: http://hdl.handle.net/10044/1/65126
DOI: 10.1109/3DV.2018.00015
ISBN: 9781538684252
ISSN: 2378-3826
Publisher: IEEE
Start Page: 32
End Page: 41
Journal / Book Title: 2018 International Conference on 3D Vision (3DV)
Replaces: 10044/1/71302
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Dyson Technology Limited
Funder's Grant Number: PO 4500501004
Conference Name: 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), International Conference on
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Publication Status: Published
Start Date: 2018-09-05
Finish Date: 2018-09-08
Conference Place: Verona, Italy
Open Access location: https://arxiv.org/pdf/1808.08378.pdf
Online Publication Date: 2018-10-15
Appears in Collections:Faculty of Engineering

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx