EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association release_cefngwywdfct3cdswvno2gzuc4

by Michael Strecke, Jörg Stückler

Released as a article .

2019  

Abstract

The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a novel approach to dynamic SLAM with dense object-level representations. We represent rigid objects in local volumetric signed distance function (SDF) maps, and formulate multi-object tracking as direct alignment of RGB-D images with the SDF representations. Our main novelty is a probabilistic formulation which naturally leads to strategies for data association and occlusion handling. We analyze our approach in experiments and demonstrate that our approach compares favorably with the state-of-the-art methods in terms of robustness and accuracy.
In text/plain format

Archived Files and Locations

application/pdf  8.2 MB
file_473ffvjmn5hprgmfh5en2sftm4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-04-26
Version   v1
Language   en ?
arXiv  1904.11781v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 7d2fc4ec-0317-41ad-90a3-017aeacd5742
API URL: JSON