Robust egomotion for large-scale trajectories

Rodríguez, Diego; Aouf, Nabil

doi:10.1109/mfi.2012.6343049

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…There are typically two reprojection strategies for motion estimation where either points from the previous pair are reprojected into the current frame or the other way round. However, as stated in [24], combining both reprojections yields better accuracy. A similar approach is adopted here.…”

Section: A Motion Parametersmentioning

confidence: 88%

Robust feature matching for aerial visual odometry

Mouats

Aouf

2014

Proceedings ELMAR-2014

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Interest points matching for aerial visual odometry using quadrotor MAV is tackled in this work. First, a set of sparse feature points are extracted using ORB detector. These are then grouped using Gradient Vector Flow (GVF) fields by finding points of high symmetry within the image. A robust matching strategy is introduced to improve the motion estimation. In order to validate ORB features matches, their grouping points are compared. Using the matched points, windowed bundle adjustment incorporating Gauss-Newton optimization is utilised for motion estimation. In order to deal with matching outliers, a Random sample consensus outlier rejection scheme is integrated. Lack of MAV stereo datasets in the literature motivated the generation of such vital data. Detailed results validating the proposed strategy are illustrated using these datasets. Also, a comparison with other approaches is also provided and shows the superiority of our approach.

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Section: A Motion Parametersmentioning

confidence: 88%

Robust feature matching for aerial visual odometry

Mouats

Aouf

2014

Proceedings ELMAR-2014

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“…Compared with the corner detectors and associated image features, the SIFT and SURF feature sets are rotationinvariant. Both the feature sets have been used in visual (IMU) odometry systems [20,25,28]. In order to obtain both blob and corner points, Geiger et al [15] introduced a blob and corner detector and a set of Sobel [33] filtered image patches based features.…”

Section: Salient Point Detectors and Local Features Used In Visual Odmentioning

confidence: 99%

Monocular visual-IMU odometry using multi-channel image patch exemplars

Dong

et al. 2016

Multimed Tools Appl

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In this paper, we propose three sets of multi-channel image patch features for monocular visual-IMU (Inertial Measurement Unit) odometry. The proposed feature sets extract image patch exemplars from multiple feature maps of an image. We also modify an existing visual-IMU odometry framework by using different salient point detectors and feature sets and replacing the inlier selection approach with a self-adaptive scheme. The modified framework is used to examine the proposed feature sets. In addition to the Root Mean Square Error (RMSE) metric, we use the Hausdorff distance to measure the inconsistency between the estimated and ground-truth trajectories. Compared to the point-wise comparison used by RMSE, the Hausdorff distance takes the shape inconsistency of two trajectories into account and is hence more perceptually consistent. Experimental results show that the multi-channel feature sets outperform, or perform comparably to, the single gray level channel feature sets examined in this study. Particularly, the multi-channel feature set that uses integral channels, i.e., ICIMGP (Integral Channel Image Patches), outperforms two state-of-the-art feature sets: SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). Besides, ICIMGP performs better than the two multi-channel feature sets that are designed based on derivative channels and gradient channels respectively. These promising results are attributed to the fact that the multi-channel features encode richer image characteristics than their single gray level channel counterparts.

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A Novel Image Representation via Local Frequency Analysis for Illumination Invariant Stereo Matching

Mouats

Aouf

Richardson

2015

IEEE Trans. on Image Process.

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In this paper, we propose a novel image representation approach to tackle illumination variations in stereo matching problems. Images are mapped using their Fourier transforms which are convolved with a set of monogenic filters. Frequency analysis is carried out at different scales to account for most image content. The phase congruency and the local weighted mean phase angle are then computed over all the scales. The original image is transformed into a new representation using these two mappings. This representation is invariant to illumination and contrast variations. More importantly, it is generic and can be used with most sparse as well as dense stereo matching algorithms. In addition, sequential feature matching or tracking can also benefit from our approach in varying radiometric conditions. We demonstrate the improvements introduced with our image mappings on well-established data sets in the literature as well as on our own experimental scenarios that include high dynamic range imagery. The experiments include both dense and sparse stereo and sequential matching algorithms where the latter is considered in the very challenging visual odometry framework.

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Robust egomotion for large-scale trajectories

Cited by 4 publications

References 21 publications

Robust feature matching for aerial visual odometry

Robust feature matching for aerial visual odometry

Monocular visual-IMU odometry using multi-channel image patch exemplars

A Novel Image Representation via Local Frequency Analysis for Illumination Invariant Stereo Matching

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