Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Robotics

arXiv:1705.10739 (cs)
[Submitted on 30 May 2017]

Title:Efficient Decentralized Visual Place Recognition From Full-Image Descriptors

Authors:Titus Cieslewski, Davide Scaramuzza
View PDF
Abstract:In this paper, we discuss the adaptation of our decentralized place recognition method described in [1] to full image descriptors. As we had shown, the key to making a scalable decentralized visual place recognition lies in exploting deterministic key assignment in a distributed key-value map. Through this, it is possible to reduce bandwidth by up to a factor of n, the robot count, by casting visual place recognition to a key-value lookup problem. In [1], we exploited this for the bag-of-words method [3], [4]. Our method of casting bag-of-words, however, results in a complex decentralized system, which has inherently worse recall than its centralized counterpart. In this paper, we instead start from the recent full-image description method NetVLAD [5]. As we show, casting this to a key-value lookup problem can be achieved with k-means clustering, and results in a much simpler system than [1]. The resulting system still has some flaws, albeit of a completely different nature: it suffers when the environment seen during deployment lies in a different distribution in feature space than the environment seen during training.
Comments: 3 pages, 4 figures. This is a self-published paper that accompanies our original work [1] as well as the ICRA 2017 Workshop on Multi-robot Perception-Driven Control and Planning [2]
Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1705.10739 [cs.RO]
  (or arXiv:1705.10739v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.1705.10739
Journal reference: 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) 78-82
Related DOI: https://doi.org/10.1109/MRS.2017.8250934

Submission history

From: Titus Cieslewski [view email]
[v1] Tue, 30 May 2017 16:43:57 UTC (2,064 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.RO
< prev   |   next >
new | recent | 2017-05
Change to browse by:
cs
cs.CV

References & Citations

DBLP - CS Bibliography

listing | bibtex
Titus Cieslewski
Davide Scaramuzza
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)