Search results for "feature matching"
showing 9 items of 9 documents
Selective visual odometry for accurate AUV localization
2015
In this paper we present a stereo visual odometry system developed for autonomous underwater vehicle localization tasks. The main idea is to make use of only highly reliable data in the estimation process, employing a robust keypoint tracking approach and an effective keyframe selection strategy, so that camera movements are estimated with high accuracy even for long paths. Furthermore, in order to limit the drift error, camera pose estimation is referred to the last keyframe, selected by analyzing the feature temporal flow. The proposed system was tested on the KITTI evaluation framework and on the New Tsukuba stereo dataset to assess its effectiveness on long tracks and different illumina…
Accurate keyframe selection and keypoint tracking for robust visual odometry
2016
This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points—higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkab…
SAMSLAM: Simulated Annealing Monocular SLAM
2013
This paper proposes a novel monocular SLAM approach. For a triplet of successive keyframes, the approach inteleaves the registration of the three 3D maps associated to each image pair in the triplet and the refinement of the corresponding poses, by progressively limiting the allowable reprojection error according to a simulated annealing scheme. This approach computes only local overlapping maps of almost constant size, thus avoiding problems of 3D map growth. It does not require global optimization, loop closure and back-correction of the poses.
RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2
2020
This study introduces an extension of the shifting gradient local orientation histogram doubled (sGLOH2) local image descriptor inspired by RootSIFT ‘square rooting’ as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state-of-the-art non-deep descriptors in recent evaluation contests dealing with both planar and non-planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that ‘square rooting’ has beneficial effects on sGLOH2 as it happe…
New Error Measures to Evaluate Features on Three-Dimensional Scenes
2011
In this paper new error measures to evaluate image features in three-dimensional scenes are proposed and reviewed. The proposed error measures are designed to take into account feature shapes, and ground truth data can be easily estimated. As other approaches, they are not error-free and a quantitative evaluation is given according to the number of wrong matches and mismatches in order to assess their validity
Improving point matching on multimodal images using distance and orientation automatic filtering
2016
International audience; Speed Up Robust Features SURF is one of the most popular and efficient methods used for image registration task. In order to achieve a correct registration, a good matching of feature point is required. However in the case of multimodal images, the high and non-linear intensity changes between different modalities led to many outliers (mismatching of detected points) and consequently a fail in the registration. Therefore, in this paper we introduce an efficient method devoted to the detection and removal of such outlier. It's based on an automatic filtering of outliers on both distance and orientation between features points. We tested our proposed method on a set of…
An automatic filtering algorithm for SURF-based registration of remote sensing images
2017
International audience; The registration of remote sensing images has been often a necessary step for further analyses of images taken at different times, different viewing geometry or with different sensors. For this task there exists many approaches. This paper focuses on the feature-based category of image registration methods. Particularly, we propose an improvement of the SURF algorithm on the point matching step. Indeed, in order to achieve a correct registration, a good matching of feature point is required. However The presence of outliers lead to a fail in the registration. Therefore, in this paper, we introduce an efficient method devoted to the detection and removal of such outli…
Views selection for SIFT based object modeling and recognition
2016
In this paper we focus on automatically learning object models in the framework of keypoint based object recognition. The proposed method uses a collection of views of the objects to build the model. For each object the collection is composed of N×M views obtained rotating the object around its vertical and horizontal axis. As keypoint based object recognition using a complete set of views is computationally expensive, we focused on the definition of a selection method that creates, for each object, a subset of the initial views that visually summarize the characteristics of the object and should be suited for recognition. We select the views by determining maxima and minima of a function, …
Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment
2013
This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.