Scene Structure Classification as Preprocessing for Feature-Based Visual Odometry

2018 ◽  
Vol 11 (3) ◽  
Author(s):  
Nathir A. Rawashdeh ◽  
Mohamed Aladem ◽  
Stan Baek ◽  
Samir A. Rawashdeh
Keyword(s):  
Visual Odometry ◽  
Scene Structure ◽  
Feature Based

scholarly journals Image Gradient-based Joint Direct Visual Odometry for Stereo Camera

2017 ◽  
Author(s):  
Jianke Zhu
Keyword(s):  
Visual Odometry ◽  
Research Problem ◽  
Feature Representation ◽  
Local Optimum ◽  
Multi Scale ◽  
Illumination Changes ◽  
Pyramid Scheme ◽  
Gradient Based ◽  
Feature Based ◽  
Dense 3D Reconstruction

Visual odometry is an important research problem for computer vision and robotics. In general, the feature-based visual odometry methods heavily rely on the accurate correspondences between local salient points, while the direct approaches could make full use of whole image and perform dense 3D reconstruction simultaneously. However, the direct visual odometry usually suffers from the drawback of getting stuck at local optimum especially with large displacement, which may lead to the inferior results. To tackle this critical problem, we propose a novel scheme for stereo odometry in this paper, which is able to improve the convergence with more accurate pose. The key of our approach is a dual Jacobian optimization that is fused into a multi-scale pyramid scheme. Moreover, we introduce a gradient-based feature representation, which enjoys the merit of being robust to illumination changes. Furthermore, a joint direct odometry approach is proposed to incorporate the information from the last frame and previous keyframes. We have conducted the experimental evaluation on the challenging KITTI odometry benchmark, whose promising results show that the proposed algorithm is very effective for stereo visual odometry.

A Modest System of Feature-based Stereo Visual Odometry

2021 ◽  
Author(s):  
William Gates ◽  
Grafika Jati ◽  
Riskyana Dewi Intan P ◽  
Mahardhika Pratama ◽  
Wisnu Jatmiko
Keyword(s):  
Visual Odometry ◽  
Feature Based ◽  
Stereo Visual Odometry

scholarly journals Efficient RGB–D data processing for feature–based self–localization of mobile robots

2016 ◽  
Vol 26 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Marek Kraft ◽  
Michał Nowicki ◽  
Rudi Penne ◽  
Adam Schmidt ◽  
Piotr Skrzypczyński
Keyword(s):  
Feature Extraction ◽  
Degrees Of Freedom ◽  
Active Vision ◽  
Visual Odometry ◽  
Data Sets ◽  
Six Degrees Of Freedom ◽  
Localization And Mapping ◽  
Feature Based ◽  
General Architecture ◽  
Mapping Solution

Abstract The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous results as to the general architecture, the important novel elements introduced here are aimed at improving the precision and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover, we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms, whereas the performance of the whole navigation system compares favorably to results known from the literature.

scholarly journals Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry

2021 ◽  
Author(s):  
Mariia Gladkova ◽  
Rui Wang ◽  
Niclas Zeller ◽  
Daniel Cremers
Keyword(s):  
Visual Odometry ◽  
Feature Based ◽  
Tight Integration
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