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Improved Visual Odometry based on Transitivity Error in Disparity Space: A Third-eye Approach

Published: 19 November 2014 Publication History

Abstract

Accurate estimation of ego-motion heavily relies on correct point correspondences in the context of visual odometry. In order to ensure a metric reconstruction of camera motion, we can refer to the 3D structure of the scene. In this paper we present an indicator for evaluating the accuracy of stereo-based 3D point measurements as well as for filtering out low-confidence correspondences for ego-motion estimation. In a typical binocular system, the left and right images are matched to produce a disparity map. For a trinocular system, however, the map can be derived indirectly via disparity maps of both cameras with respect to the third camera. The difference between an explicitly matched disparity map and its indirect construction defines a transitivity error in disparity space (TED).
We evaluate the effectiveness of TED from different perspectives, using a trinocular vehicle-mounted vision system. Results presented in 3D Euclidean space, or in 2D images show improvements of more than 7.5%, indicating that, by taking TED into account, more consistency is ensured for ego-motion estimation.

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Cited By

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  • (2019)Effects of Ground Manifold Modeling on the Accuracy of Stixel CalculationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.287942920:10(3675-3687)Online publication date: Oct-2019
  • (2018)Use of a Confidence Map Towards Improved Multi-layer Stixel Segmentation2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2018.8639171(1-6)Online publication date: Nov-2018
  • (2017)Improved Stixel Estimation Based on Transitivity Analysis in Disparity SpaceComputer Analysis of Images and Patterns10.1007/978-3-319-64689-3_3(28-40)Online publication date: 28-Jul-2017

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  1. Improved Visual Odometry based on Transitivity Error in Disparity Space: A Third-eye Approach

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      IVCNZ '14: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand
      November 2014
      298 pages
      ISBN:9781450331845
      DOI:10.1145/2683405
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • The University of Waikato

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 19 November 2014

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      IVCNZ '14 Paper Acceptance Rate 55 of 74 submissions, 74%;
      Overall Acceptance Rate 55 of 74 submissions, 74%

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      View all
      • (2019)Effects of Ground Manifold Modeling on the Accuracy of Stixel CalculationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.287942920:10(3675-3687)Online publication date: Oct-2019
      • (2018)Use of a Confidence Map Towards Improved Multi-layer Stixel Segmentation2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2018.8639171(1-6)Online publication date: Nov-2018
      • (2017)Improved Stixel Estimation Based on Transitivity Analysis in Disparity SpaceComputer Analysis of Images and Patterns10.1007/978-3-319-64689-3_3(28-40)Online publication date: 28-Jul-2017

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