An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter (PM
) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a ...mismatch in spatiotemporal resolution, integration of AOD and PM
data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed PM
and AOD data, were used for mapping of PM
over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution (DT
) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) "Optical_Depth_Land_And_Ocean". AOD observations were also obtained from the merged DT and DB (deep blue) product (DTB
) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-PM
with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of PM
from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after PM
mapping. The hotspot and spatial variability of PM
maps can give us a summary of the spatiotemporal patterns of PM
variations.
Accurate Dynamic SLAM Using CRF-Based Long-Term Consistency Du, Zheng-Jun; Huang, Shi-Sheng; Mu, Tai-Jiang ...
IEEE transactions on visualization and computer graphics,
2022-April-1, 2022-Apr, 2022-4-1, 20220401, Letnik:
28, Številka:
4
Journal Article
Recenzirano
Accurate camera pose estimation is essential and challenging for real world dynamic 3D reconstruction and augmented reality applications. In this article, we present a novel RGB-D SLAM approach for ...accurate camera pose tracking in dynamic environments. Previous methods detect dynamic components only across a short time-span of consecutive frames. Instead, we provide a more accurate dynamic 3D landmark detection method, followed by the use of long-term consistency via conditional random fields, which leverages long-term observations from multiple frames. Specifically, we first introduce an efficient initial camera pose estimation method based on distinguishing dynamic from static points using graph-cut RANSAC. These static/dynamic labels are used as priors for the unary potential in the conditional random fields, which further improves the accuracy of dynamic 3D landmark detection. Evaluation using the TUM and Bonn RGB-D dynamic datasets shows that our approach significantly outperforms state-of-the-art methods, providing much more accurate camera trajectory estimation in a variety of highly dynamic environments. We also show that dynamic 3D reconstruction can benefit from the camera poses estimated by our RGB-D SLAM approach.
Visual Simultaneous Localization and Mapping (SLAM) based on RGB-D data has developed as a fundamental approach for robot perception over the past decades. There is an extensive literature regarding ...RGB-D SLAM and its applications. However, most of existing RGB-D SLAM methods assume that the traversed environments are static during the SLAM process. This is because moving objects in dynamic environments can severely degrade the SLAM performance. The static world assumption limits the applications of RGB-D SLAM in dynamic environments. In order to address this problem, we proposed a novel RGB-D data-based motion removal approach and integrated it into the front end of RGB-D SLAM. The motion removal approach acted as a pre-processing stage to filter out data that were associated with moving objects. We conducted experiments using a public RGB-D dataset. The results demonstrated that the proposed motion removal approach was able to effectively improve RGB-D SLAM in various challenging dynamic environments.
•We propose a motion removal approach with a freely moving RGB-D camera.•Comparative results with and without motion removal using TUM dataset are given.•The proposed motion removal approach benefits RGB-D SLAM in dynamic environments.
For the speed of traditional SIFT algorithm in the feature extraction and matching is slow, the article proposes an improved RANSAC features image matching method based on speeded up robust features ...(SURF). First of all, detect images features and extract with SURF method, use the fast library for approximate nearest neighbours-based matcher method to perform initial matching on image feature points. Improve the RANSAC algorithm to increase the probability of correct matching points being sampled. Experimental results show that the improved RANSAC algorithm has high matching accuracy, good robustness, and short running time. It lays the foundation for the subsequent fast image stitching.
As the "lifeline" of urban operation, buried pipelines play an essential role in modern society. Due to the lack of comprehensive pipeline information, pipeline damage accidents often occur in urban ...construction. This article proposes a ground-penetrating radar (GPR)-based mapping method, including image processing and target detection technique, which applies to underground pipeline networks. The main process of this method is to pre-process the echo data by reasonably arranging the measurement line, fit the hyperbola to extract the peak vertex, and finally fit multiple detection points to realize the pipeline distribution fitting. An innovative fitting method based on random sample consensus (RANSAC) and the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means++ algorithm was proposed for GPR testing. The buried depth was estimated and combined with the fitting results to map the underground pipeline network distribution. The effectiveness of the proposed innovative pipeline fitting method was verified through numerical simulation. Then, in situ experiments were conducted on single and multiple pipelines. The numerical and experimental results show that the proposed method can accurately locate buried pipes and effectively reconstruct the direction distribution of underground pipeline networks.
SIFT Matching by Context Exposed Bellavia, Fabio
IEEE transactions on pattern analysis and machine intelligence,
2023-Feb.-1, 2023-Feb, 2023-2-1, 20230201, Letnik:
45, Številka:
2
Journal Article
Recenzirano
Odprti dostop
This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space ...and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetric matching, enabling to achieve a global improvement upon each individual strategy. DTM alternates between Delaunay triangulation contractions and expansions to figure out and adjust keypoint neighborhood consistency. Experimental evaluation shows that DTM is comparable or better than the state-of-the-art in terms of matching accuracy and robustness. Evaluation is carried out according to a new benchmark devised for analyzing the matching pipeline in terms of correct correspondences on both planar and non-planar scenes, including several state-of-the-art methods as well as the common SIFT matching approach for reference. This evaluation can be of assistance for future research in this field.
•The pixel shift clustering, a new mismatches detection, is proposed.•The pixel shift models of feature point are established for different motion patterns.•Mismatches are eliminated by density peaks ...clustering.•The test use indoor, outdoor and kitti database, then compared with the related works.•The proposed method shows good performance in matching accuracy and robustness.
This paper focuses on improving the accuracy of image matching by eliminating the residual mismatches in the matching results of standard RANSAC. Based on pixel shift clustering and RANSAC algorithms, a matching optimization algorithm called pixel shift clustering RANSAC, PSC-RANSAC in short, is proposed in this paper. Firstly, the pixel shift model of space point from two perspectives are established by parallax principle and camera projection model. Then, based on the established pixel shift model, density peaks clustering (DPC) algorithm is used to select the mismatches out to enhance the accuracy of image matching. Meanwhile the comparisons among PSC-RANSAC, standard RANSAC, progressive sample consensus and graph-cut RANSAC show that PSC-RANSAC can more effectively and robustly eliminate the residual mismatches in initial matching results. The proposed method provides an effective tool for optimization on image matching.
The ability to handle outliers is essential for performing the perspective-
n
-point (P
n
P) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time ...complexities. We propose a fast P
n
P solution named R1PP
n
P to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P
n
P algorithm, which serves as the core of R1PP
n
P, for solving the P
n
P problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PP
n
P is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PP
n
P is among the most accurate and fast P
n
P solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP
n
P, which is the state-of-the-art P
n
P algorithm with an explicit outliers-handling mechanism, R1PP
n
P is slower but does not suffer from the percentage of outliers limitation as REPP
n
P.
We describe a learning-based system that estimates the camera position and orientation from a single input image relative to a known environment. The system is flexible w.r.t. the amount of ...information available at test and at training time, catering to different applications. Input images can be RGB-D or RGB, and a 3D model of the environment can be utilized for training but is not necessary. In the minimal case, our system requires only RGB images and ground truth poses at training time, and it requires only a single RGB image at test time. The framework consists of a deep neural network and fully differentiable pose optimization. The neural network predicts so called scene coordinates, i.e., dense correspondences between the input image and 3D scene space of the environment. The pose optimization implements robust fitting of pose parameters using differentiable RANSAC ( DSAC ) to facilitate end-to-end training. The system, an extension of DSAC++ and referred to as DSAC*, achieves state-of-the-art accuracy on various public datasets for RGB-based re-localization, and competitive accuracy for RGB-D based re-localization.
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