Visual odometry using a homography formulation with decoupled rotation and translation estimation using minimal solutions

Abstract : In this paper we present minimal solutions for two-view relative motion estimation based on a homography formulation. By assuming a known vertical direction (e.g. from an IMU) and assuming a dominant ground plane we demonstrate that rotation and translation estimation can be decoupled. This result allows us to reduce the number of point matches needed to compute a motion hypothesis. We then derive different algorithms based on this decoupling that allow an efficient estimation. We also demonstrate how these algorithms can be used efficiently to compute an optimal inlier set using exhaustive search or histogram voting instead of a traditional RANSAC step. Our methods are evaluated on synthetic data and on the KITTI data set, demonstrating that our methods are well suited for visual odometry in road driving scenarios.
Liste complète des métadonnées

Cited literature [16 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01756773
Contributor : Cédric Demonceaux <>
Submitted on : Tuesday, April 3, 2018 - 8:41:20 AM
Last modification on : Thursday, February 7, 2019 - 5:27:49 PM

File

ICRA2018_FinalVersion.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01756773, version 1

Citation

Banglei Guan, Pascal Vasseur, Cédric Demonceaux, Friedrich Fraundorfer. Visual odometry using a homography formulation with decoupled rotation and translation estimation using minimal solutions. International Conference on Robotics and Automation - ICRA, May 2018, Brisbane, Australia. ⟨hal-01756773⟩

Share

Metrics

Record views

376

Files downloads

914