Robust Dense Visual Odometry For RGB-D Cameras In A Dynamic Environment

Abstract : —The aim of our work is to estimate the camera motion from RGB-D images in a dynamic scene. Most of the existing methods have a poor localization performance in such environments, which makes them inapplicable in real world conditions. In this paper, we propose a new dense visual odometry method that uses RANSAC to cope with dynamic scenes. We show the efficiency and robustness of the proposed method on a large set of experiments in challenging situations and from publicly available benchmark dataset. Additionally, we compare our approach to another state-of-art method based on M-estimator that is used to deal with dynamic scenes. Our method gives similar results on benchmark sequences and better results on our own dataset.
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.inria.fr/hal-01212043
Contributor : Dib Abdallah <>
Submitted on : Thursday, October 8, 2015 - 9:54:13 AM
Last modification on : Tuesday, December 18, 2018 - 4:40:22 PM
Long-term archiving on : Saturday, January 9, 2016 - 10:11:42 AM

File

preprint.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01212043, version 1

Collections

Citation

Abdallah Dib, François Charpillet. Robust Dense Visual Odometry For RGB-D Cameras In A Dynamic Environment. International Conference on Advanced Robotics ICAR 2015, Jul 2015, Istanbul, Turkey. ⟨hal-01212043⟩

Share

Metrics

Record views

417

Files downloads

551