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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Place recognition in semi-dense maps: Geometric and learning-based approaches
Organization Unit
  • Yawei Ye
  • Titus Cieslewski
  • Antonio Loquercio
  • Davide Scaramuzza
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Page Range 1 - 8
Event Title British Machine Vision Conference
Event Type conference
Event Location London
Event Start Date September 4 - 2017
Event End Date September 7 - 2017
Place of Publication British Machine Vision Conference
Abstract Text For robotics and augmented reality systems operating in large and dynamic environments, place recognition and tracking using vision represent very challenging tasks. Additionally, when these systems need to reliably operate for very long time periods, such as months or years, further challenges are introduced by severe environmental changes, that can significantly alter the visual appearance of a scene. Thus, to unlock long term, large scale visual place recognition, it is necessary to develop new methodologies for improving localization under difficult conditions. As shown in previous work, gains in robustness can be achieved by exploiting the 3D structural information of a scene. The latter, extracted from image sequences, carries in fact more discriminative clues than individual images only. In this paper, we propose to represent a scene’s structure with semi-dense point clouds, due to their highly informative power, and the simplicity of their generation through mature visual odometry and SLAM systems. Then we cast place recognition as an instance of pose retrieval and evaluate several techniques, including recent learning based approaches, to produce discriminative descriptors of semi-dense point clouds. Our proposed methodology, evaluated on the recently published and challenging Oxford Robotcar Dataset, shows to outperform image-based place recognition, with improvements up to 30% in precision across strong appearance changes. To the best of our knowledge, we are the first to propose place recognition in semi-dense maps.
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