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ADVIO: An Authentic Dataset for Visual-Inertial Odometry

  • Santiago Cortés
  • Arno Solin
  • Esa Rahtu
  • Juho Kannala
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11214)

Abstract

The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.

Keywords

Visual-inertial odometry Navigation Benchmarking 

Supplementary material

474197_1_En_26_MOESM1_ESM.pdf (933 kb)
Supplementary material 1 (pdf 932 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAalto UniversityEspooFinland
  2. 2.Tampere University of TechnologyTampereFinland

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