Loop Closure Detection via Locality Preserving Matching With Global Consensus

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.

Image Enlargement Via Local Matching

This paper gives a new algorithm to enlarge images based on local matching. Its main advantage is capable of preserving the edge of the enlarged image and improving both the subjective effect and the objective effect.

Local algorithms on dense two-frame stereo matching

Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps： cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.

Source-Space Compressive Matched Field Processing for Source Localization

Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.

Object detection based on combination of local and spatial information

A method of object detection based on combination of local and spatial information is proposed. Firstly, the categorygiven representative images are chosen through clustering to be templates, and the local and spatial information of template are ex- tracted and generalized as the template feature. At the same time, the codebook dictionary of local contour is also built up. Secondly, based on the codebook dictionary, sliding-window mechanism and the vote algorithm are used to select initial candidate object win- dows. Lastly, the final object windows are got from initial candidate windows based on local and spatial structure feature matching. Experimental results demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.

Research on Damage Localization of Plate-like Structure Using Mode Acoustic Emission and Matching Pursuit

Based on mode acoustic emission theory,the paper analyses the acoustic emission analog signal of thin steel plate using matching pursuit,then obtains the characteristics interpretation of the different frequency signal energy concentration degree; Combined with four-point arc positioning method,the papers researches the damage localization of the plate-like structure. Simulation experiment shows that this method can accurately detect and locate the damage. This can provide data support for further imaging research based on time reverse theory.

Bayesian localization in an uncertain ocean environment

In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density（PPD） over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy.

Three-dimensional sound source localization using distributed microphone arrays

To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.

Matched localization of a horizontal array in shallow sea

Matched-field processing (MFP) and matched-mode processing (MMP) are passive range and depth source localization technique that have been extensively used in shallow-water nvironments. In this paper a new technique of normal-mode filtering and the simulated results on source localization of a short horizontal array are presented. The results of MMP are compared to those obtained by conventional MFP. The simulated results indicate that for MMP source location the resolution is higher than that of MFP's and the sidelobe rejection is improved, the expense of the computational time is less. The effects of system mismatching are also given.