A Robust Eye Localization Method for Low Quality Face Images

物体位姿估计综述英文Overview of Object Pose Estimation.Object pose estimation is a crucial task in computer vision, aiming to determine the position and orientation of objects in a given scene. It plays a pivotal role in various applications, ranging from augmented reality to robotics and autonomous driving. This article presents a comprehensive overview of object pose estimation, discussing its importance, methods, challenges, and future trends.Importance of Object Pose Estimation.Object pose estimation is essential for understanding and interacting with the physical world. It enables systems to perceive the three-dimensional position and orientation of objects accurately, enabling precise manipulation, localization, and tracking. In augmented reality, pose estimation is crucial for overlaying virtual objects ontothe real world. In robotics, it enables robots to grasp, manipulate, and interact with objects effectively. In autonomous driving, pose estimation is vital for perceiving the position and orientation of vehicles and pedestrians to ensure safe navigation.Methods of Object Pose Estimation.Object pose estimation can be categorized into two broad approaches: template-based methods and learning-based methods.Template-based methods involve the creation of a 3D model or template of the object and matching it with the observed 2D image to estimate the pose. One popular algorithm is the Iterative Closest Point (ICP), whichaligns the 3D model with the 2D image by minimizing the distances between corresponding points. Template-based methods are accurate but computationally expensive and limited to known object categories.Learning-based methods, on the other hand, utilize deeplearning techniques to learn pose estimation directly from data. Convolutional Neural Networks (CNNs) are commonly used to extract features from images, and pose estimationis performed using regression or classification tasks. Methods like PoseNet and PVNet have achieved remarkable results in recent years. Learning-based methods are more flexible and can handle unknown object categories but require large amounts of labeled data for training.Challenges in Object Pose Estimation.Object pose estimation faces several challenges, including occlusion, cluttered scenes, and varying lighting conditions. Occlusion occurs when objects overlap or are partially hidden, making it difficult to extract sufficient information for pose estimation. Cluttered scenes present a challenge due to the presence of multiple objects, makingit difficult to separate and identify individual objects. Varying lighting conditions can affect the appearance of objects, leading to inaccuracies in pose estimation.Another challenge is the diversity of object shapes andsizes. Different objects have unique geometric properties that require specific approaches for accurate pose estimation. Additionally, pose estimation is oftensensitive to noise and outliers in the input data, which can affect the accuracy of the estimated pose.Future Trends in Object Pose Estimation.With the advancements in deep learning and computer vision, object pose estimation is expected to evolvefurther in the coming years. One promising direction is the integration of sensor data, such as depth sensors or RGB-D cameras, to enhance pose estimation accuracy in complex environments. Multi-modal data fusion can provideadditional information about object geometry and depth, leading to more robust pose estimation.Another trend is the utilization of larger and more diverse datasets for training deep learning models. This will enable the development of more generalizable and robust pose estimation algorithms that can handle a wide range of object categories and environments.Finally, real-time pose estimation is an important direction for future research. Many applications, such as augmented reality and robotics, require pose estimation to be performed in real-time, enabling fast and responsive interactions. The development of efficient algorithms and hardware optimizations can lead to significant improvements in real-time pose estimation capabilities.In conclusion, object pose estimation is a crucial task in computer vision with widespread applications. It involves the estimation of the position and orientation of objects in a given scene, enabling precise manipulation, localization, and tracking. Template-based and learning-based methods are commonly used for pose estimation, each with its own advantages and limitations. Challenges such as occlusion, cluttered scenes, and varying lighting conditions need to be addressed to improve pose estimation accuracy. Future trends include the integration of sensor data, utilization of larger and more diverse datasets, and the development of real-time pose estimation algorithms. With continued research and advancements in this field,object pose estimation is expected to play a pivotal role in enabling more intelligent and responsive interactions with the physical world.。

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A Fast and Accurate Plane Detection Algorithm for Large Noisy Point CloudsUsing Filtered Normals and Voxel GrowingJean-Emmanuel DeschaudFranc¸ois GouletteMines ParisTech,CAOR-Centre de Robotique,Math´e matiques et Syst`e mes60Boulevard Saint-Michel75272Paris Cedex06jean-emmanuel.deschaud@mines-paristech.fr francois.goulette@mines-paristech.frAbstractWith the improvement of3D scanners,we produce point clouds with more and more points often exceeding millions of points.Then we need a fast and accurate plane detection algorithm to reduce data size.In this article,we present a fast and accurate algorithm to detect planes in unorganized point clouds usingfiltered normals and voxel growing.Our work is based on afirst step in estimating better normals at the data points,even in the presence of noise.In a second step,we compute a score of local plane in each point.Then, we select the best local seed plane and in a third step start a fast and robust region growing by voxels we call voxel growing.We have evaluated and tested our algorithm on different kinds of point cloud and compared its performance to other algorithms.1.IntroductionWith the growing availability of3D scanners,we are now able to produce large datasets with millions of points.It is necessary to reduce data size,to decrease the noise and at same time to increase the quality of the model.It is in-teresting to model planar regions of these point clouds by planes.In fact,plane detection is generally afirst step of segmentation but it can be used for many applications.It is useful in computer graphics to model the environnement with basic geometry.It is used for example in modeling to detect building facades before classification.Robots do Si-multaneous Localization and Mapping(SLAM)by detect-ing planes of the environment.In our laboratory,we wanted to detect small and large building planes in point clouds of urban environments with millions of points for modeling. As mentioned in[6],the accuracy of the plane detection is important for after-steps of the modeling pipeline.We also want to be fast to be able to process point clouds with mil-lions of points.We present a novel algorithm based on re-gion growing with improvements in normal estimation and growing process.For our method,we are generic to work on different kinds of data like point clouds fromfixed scan-ner or from Mobile Mapping Systems(MMS).We also aim at detecting building facades in urban point clouds or little planes like doors,even in very large data sets.Our input is an unorganized noisy point cloud and with only three”in-tuitive”parameters,we generate a set of connected compo-nents of planar regions.We evaluate our method as well as explain and analyse the significance of each parameter. 2.Previous WorksAlthough there are many methods of segmentation in range images like in[10]or in[3],three have been thor-oughly studied for3D point clouds:region-growing, hough-transform from[14]and Random Sample Consen-sus(RANSAC)from[9].The application of recognising structures in urban laser point clouds is frequent in literature.Bauer in[4]and Boulaassal in[5]detect facades in dense3D point cloud by a RANSAC algorithm.V osselman in[23]reviews sur-face growing and3D hough transform techniques to de-tect geometric shapes.Tarsh-Kurdi in[22]detect roof planes in3D building point cloud by comparing results on hough-transform and RANSAC algorithm.They found that RANSAC is more efficient than thefirst one.Chao Chen in[6]and Yu in[25]present algorithms of segmentation in range images for the same application of detecting planar regions in an urban scene.The method in[6]is based on a region growing algorithm in range images and merges re-sults in one labelled3D point cloud.[25]uses a method different from the three we have cited:they extract a hi-erarchical subdivision of the input image built like a graph where leaf nodes represent planar regions.There are also other methods like bayesian techniques. In[16]and[8],they obtain smoothed surface from noisy point clouds with objects modeled by probability distribu-tions and it seems possible to extend this idea to point cloud segmentation.But techniques based on bayesian statistics need to optimize global statistical model and then it is diffi-cult to process points cloud larger than one million points.We present below an analysis of the two main methods used in literature:RANSAC and region-growing.Hough-transform algorithm is too time consuming for our applica-tion.To compare the complexity of the algorithm,we take a point cloud of size N with only one plane P of size n.We suppose that we want to detect this plane P and we define n min the minimum size of the plane we want to detect.The size of a plane is the area of the plane.If the data density is uniform in the point cloud then the size of a plane can be specified by its number of points.2.1.RANSACRANSAC is an algorithm initially developped by Fis-chler and Bolles in[9]that allows thefitting of models with-out trying all possibilities.RANSAC is based on the prob-ability to detect a model using the minimal set required to estimate the model.To detect a plane with RANSAC,we choose3random points(enough to estimate a plane).We compute the plane parameters with these3points.Then a score function is used to determine how the model is good for the remaining ually,the score is the number of points belonging to the plane.With noise,a point belongs to a plane if the distance from the point to the plane is less than a parameter γ.In the end,we keep the plane with the best score.Theprobability of getting the plane in thefirst trial is p=(nN )3.Therefore the probability to get it in T trials is p=1−(1−(nN )3)ing equation1and supposing n minN1,we know the number T min of minimal trials to have a probability p t to get planes of size at least n min:T min=log(1−p t)log(1−(n minN))≈log(11−p t)(Nn min)3.(1)For each trial,we test all data points to compute the score of a plane.The RANSAC algorithm complexity lies inO(N(Nn min )3)when n minN1and T min→0whenn min→N.Then RANSAC is very efficient in detecting large planes in noisy point clouds i.e.when the ratio n minN is 1but very slow to detect small planes in large pointclouds i.e.when n minN 1.After selecting the best model,another step is to extract the largest connected component of each plane.Connnected components mean that the min-imum distance between each point of the plane and others points is smaller(for distance)than afixed parameter.Schnabel et al.[20]bring two optimizations to RANSAC:the points selection is done locally and the score function has been improved.An octree isfirst created from point cloud.Points used to estimate plane parameters are chosen locally at a random depth of the octree.The score function is also different from RANSAC:instead of testing all points for one model,they test only a random subset and find the score by interpolation.The algorithm complexity lies in O(Nr4Ndn min)where r is the number of random subsets for the score function and d is the maximum octree depth. Their algorithm improves the planes detection speed but its complexity lies in O(N2)and it becomes slow on large data sets.And again we have to extract the largest connected component of each plane.2.2.Region GrowingRegion Growing algorithms work well in range images like in[18].The principle of region growing is to start with a seed region and to grow it by neighborhood when the neighbors satisfy some conditions.In range images,we have the neighbors of each point with pixel coordinates.In case of unorganized3D data,there is no information about the neighborhood in the data structure.The most common method to compute neighbors in3D is to compute a Kd-tree to search k nearest neighbors.The creation of a Kd-tree lies in O(NlogN)and the search of k nearest neighbors of one point lies in O(logN).The advantage of these region growing methods is that they are fast when there are many planes to extract,robust to noise and extract the largest con-nected component immediately.But they only use the dis-tance from point to plane to extract planes and like we will see later,it is not accurate enough to detect correct planar regions.Rabbani et al.[19]developped a method of smooth area detection that can be used for plane detection.Theyfirst estimate the normal of each point like in[13].The point with the minimum residual starts the region growing.They test k nearest neighbors of the last point added:if the an-gle between the normal of the point and the current normal of the plane is smaller than a parameterαthen they add this point to the smooth region.With Kd-tree for k nearest neighbors,the algorithm complexity is in O(N+nlogN). The complexity seems to be low but in worst case,when nN1,example for facade detection in point clouds,the complexity becomes O(NlogN).3.Voxel Growing3.1.OverviewIn this article,we present a new algorithm adapted to large data sets of unorganized3D points and optimized to be accurate and fast.Our plane detection method works in three steps.In thefirst part,we compute a better esti-mation of the normal in each point by afiltered weighted planefitting.In a second step,we compute the score of lo-cal planarity in each point.We select the best seed point that represents a good seed plane and in the third part,we grow this seed plane by adding all points close to the plane.Thegrowing step is based on a voxel growing algorithm.The filtered normals,the score function and the voxel growing are innovative contributions of our method.As an input,we need dense point clouds related to the level of detail we want to detect.As an output,we produce connected components of planes in the point cloud.This notion of connected components is linked to the data den-sity.With our method,the connected components of planes detected are linked to the parameter d of the voxel grid.Our method has 3”intuitive”parameters :d ,area min and γ.”intuitive”because there are linked to physical mea-surements.d is the voxel size used in voxel growing and also represents the connectivity of points in detected planes.γis the maximum distance between the point of a plane and the plane model,represents the plane thickness and is linked to the point cloud noise.area min represents the minimum area of planes we want to keep.3.2.Details3.2.1Local Density of Point CloudsIn a first step,we compute the local density of point clouds like in [17].For that,we find the radius r i of the sphere containing the k nearest neighbors of point i .Then we cal-culate ρi =kπr 2i.In our experiments,we find that k =50is a good number of neighbors.It is important to know the lo-cal density because many laser point clouds are made with a fixed resolution angle scanner and are therefore not evenly distributed.We use the local density in section 3.2.3for the score calculation.3.2.2Filtered Normal EstimationNormal estimation is an important part of our algorithm.The paper [7]presents and compares three normal estima-tion methods.They conclude that the weighted plane fit-ting or WPF is the fastest and the most accurate for large point clouds.WPF is an idea of Pauly and al.in [17]that the fitting plane of a point p must take into consider-ation the nearby points more than other distant ones.The normal least square is explained in [21]and is the mini-mum of ki =1(n p ·p i +d )2.The WPF is the minimum of ki =1ωi (n p ·p i +d )2where ωi =θ( p i −p )and θ(r )=e −2r 2r2i .For solving n p ,we compute the eigenvec-tor corresponding to the smallest eigenvalue of the weightedcovariance matrix C w = ki =1ωi t (p i −b w )(p i −b w )where b w is the weighted barycenter.For the three methods ex-plained in [7],we get a good approximation of normals in smooth area but we have errors in sharp corners.In fig-ure 1,we have tested the weighted normal estimation on two planes with uniform noise and forming an angle of 90˚.We can see that the normal is not correct on the corners of the planes and in the red circle.To improve the normal calculation,that improves the plane detection especially on borders of planes,we propose a filtering process in two phases.In a first step,we com-pute the weighted normals (WPF)of each point like we de-scribed it above by minimizing ki =1ωi (n p ·p i +d )2.In a second step,we compute the filtered normal by us-ing an adaptive local neighborhood.We compute the new weighted normal with the same sum minimization but keep-ing only points of the neighborhood whose normals from the first step satisfy |n p ·n i |>cos (α).With this filtering step,we have the same results in smooth areas and better results in sharp corners.We called our normal estimation filtered weighted plane fitting(FWPF).Figure 1.Weighted normal estimation of two planes with uniform noise and with 90˚angle between them.We have tested our normal estimation by computing nor-mals on synthetic data with two planes and different angles between them and with different values of the parameter α.We can see in figure 2the mean error on normal estimation for WPF and FWPF with α=20˚,30˚,40˚and 90˚.Us-ing α=90˚is the same as not doing the filtering step.We see on Figure 2that α=20˚gives smaller error in normal estimation when angles between planes is smaller than 60˚and α=30˚gives best results when angle between planes is greater than 60˚.We have considered the value α=30˚as the best results because it gives the smaller mean error in normal estimation when angle between planes vary from 20˚to 90˚.Figure 3shows the normals of the planes with 90˚angle and better results in the red circle (normals are 90˚with the plane).3.2.3The score of local planarityIn many region growing algorithms,the criteria used for the score of the local fitting plane is the residual,like in [18]or [19],i.e.the sum of the square of distance from points to the plane.We have a different score function to estimate local planarity.For that,we first compute the neighbors N i of a point p with points i whose normals n i are close toFigure parison of mean error in normal estimation of two planes with α=20˚,30˚,40˚and 90˚(=Nofiltering).Figure 3.Filtered Weighted normal estimation of two planes with uniform noise and with 90˚angle between them (α=30˚).the normal n p .More precisely,we compute N i ={p in k neighbors of i/|n i ·n p |>cos (α)}.It is a way to keep only the points which are probably on the local plane before the least square fitting.Then,we compute the local plane fitting of point p with N i neighbors by least squares like in [21].The set N i is a subset of N i of points belonging to the plane,i.e.the points for which the distance to the local plane is smaller than the parameter γ(to consider the noise).The score s of the local plane is the area of the local plane,i.e.the number of points ”in”the plane divided by the localdensity ρi (seen in section 3.2.1):the score s =card (N i)ρi.We take into consideration the area of the local plane as the score function and not the number of points or the residual in order to be more robust to the sampling distribution.3.2.4Voxel decompositionWe use a data structure that is the core of our region growing method.It is a voxel grid that speeds up the plane detection process.V oxels are small cubes of length d that partition the point cloud space.Every point of data belongs to a voxel and a voxel contains a list of points.We use the Octree Class Template in [2]to compute an Octree of the point cloud.The leaf nodes of the graph built are voxels of size d .Once the voxel grid has been computed,we start the plane detection algorithm.3.2.5Voxel GrowingWith the estimator of local planarity,we take the point p with the best score,i.e.the point with the maximum area of local plane.We have the model parameters of this best seed plane and we start with an empty set E of points belonging to the plane.The initial point p is in a voxel v 0.All the points in the initial voxel v 0for which the distance from the seed plane is less than γare added to the set E .Then,we compute new plane parameters by least square refitting with set E .Instead of growing with k nearest neighbors,we grow with voxels.Hence we test points in 26voxel neigh-bors.This is a way to search the neighborhood in con-stant time instead of O (logN )for each neighbor like with Kd-tree.In a neighbor voxel,we add to E the points for which the distance to the current plane is smaller than γand the angle between the normal computed in each point and the normal of the plane is smaller than a parameter α:|cos (n p ,n P )|>cos (α)where n p is the normal of the point p and n P is the normal of the plane P .We have tested different values of αand we empirically found that 30˚is a good value for all point clouds.If we added at least one point in E for this voxel,we compute new plane parameters from E by least square fitting and we test its 26voxel neigh-bors.It is important to perform plane least square fitting in each voxel adding because the seed plane model is not good enough with noise to be used in all voxel growing,but only in surrounding voxels.This growing process is faster than classical region growing because we do not compute least square for each point added but only for each voxel added.The least square fitting step must be computed very fast.We use the same method as explained in [18]with incre-mental update of the barycenter b and covariance matrix C like equation 2.We know with [21]that the barycen-ter b belongs to the least square plane and that the normal of the least square plane n P is the eigenvector of the smallest eigenvalue of C .b0=03x1C0=03x3.b n+1=1n+1(nb n+p n+1).C n+1=C n+nn+1t(pn+1−b n)(p n+1−b n).(2)where C n is the covariance matrix of a set of n points,b n is the barycenter vector of a set of n points and p n+1is the (n+1)point vector added to the set.This voxel growing method leads to a connected com-ponent set E because the points have been added by con-nected voxels.In our case,the minimum distance between one point and E is less than parameter d of our voxel grid. That is why the parameter d also represents the connectivity of points in detected planes.3.2.6Plane DetectionTo get all planes with an area of at least area min in the point cloud,we repeat these steps(best local seed plane choice and voxel growing)with all points by descending order of their score.Once we have a set E,whose area is bigger than area min,we keep it and classify all points in E.4.Results and Discussion4.1.Benchmark analysisTo test the improvements of our method,we have em-ployed the comparative framework of[12]based on range images.For that,we have converted all images into3D point clouds.All Point Clouds created have260k points. After our segmentation,we project labelled points on a seg-mented image and compare with the ground truth image. We have chosen our three parameters d,area min andγby optimizing the result of the10perceptron training image segmentation(the perceptron is portable scanner that pro-duces a range image of its environment).Bests results have been obtained with area min=200,γ=5and d=8 (units are not provided in the benchmark).We show the re-sults of the30perceptron images segmentation in table1. GT Regions are the mean number of ground truth planes over the30ground truth range images.Correct detection, over-segmentation,under-segmentation,missed and noise are the mean number of correct,over,under,missed and noised planes detected by methods.The tolerance80%is the minimum percentage of points we must have detected comparing to the ground truth to have a correct detection. More details are in[12].UE is a method from[12],UFPR is a method from[10]. It is important to notice that UE and UFPR are range image methods and our method is not well suited for range images but3D Point Cloud.Nevertheless,it is a good benchmark for comparison and we see in table1that the accuracy of our method is very close to the state of the art in range image segmentation.To evaluate the different improvements of our algorithm, we have tested different variants of our method.We have tested our method without normals(only with distance from points to plane),without voxel growing(with a classical region growing by k neighbors),without our FWPF nor-mal estimation(with WPF normal estimation),without our score function(with residual score function).The compari-son is visible on table2.We can see the difference of time computing between region growing and voxel growing.We have tested our algorithm with and without normals and we found that the accuracy cannot be achieved whithout normal computation.There is also a big difference in the correct de-tection between WPF and our FWPF normal estimation as we can see in thefigure4.Our FWPF normal brings a real improvement in border estimation of planes.Black points in thefigure are non classifiedpoints.Figure5.Correct Detection of our segmentation algorithm when the voxel size d changes.We would like to discuss the influence of parameters on our algorithm.We have three parameters:area min,which represents the minimum area of the plane we want to keep,γ,which represents the thickness of the plane(it is gener-aly closely tied to the noise in the point cloud and espe-cially the standard deviationσof the noise)and d,which is the minimum distance from a point to the rest of the plane. These three parameters depend on the point cloud features and the desired segmentation.For example,if we have a lot of noise,we must choose a highγvalue.If we want to detect only large planes,we set a large area min value.We also focus our analysis on the robustess of the voxel size d in our algorithm,i.e.the ratio of points vs voxels.We can see infigure5the variation of the correct detection when we change the value of d.The method seems to be robust when d is between4and10but the quality decreases when d is over10.It is due to the fact that for a large voxel size d,some planes from different objects are merged into one plane.GT Regions Correct Over-Under-Missed Noise Duration(in s)detection segmentation segmentationUE14.610.00.20.3 3.8 2.1-UFPR14.611.00.30.1 3.0 2.5-Our method14.610.90.20.1 3.30.7308Table1.Average results of different segmenters at80%compare tolerance.GT Regions Correct Over-Under-Missed Noise Duration(in s) Our method detection segmentation segmentationwithout normals14.6 5.670.10.19.4 6.570 without voxel growing14.610.70.20.1 3.40.8605 without FWPF14.69.30.20.1 5.0 1.9195 without our score function14.610.30.20.1 3.9 1.2308 with all improvements14.610.90.20.1 3.30.7308 Table2.Average results of variants of our segmenter at80%compare tolerance.4.1.1Large scale dataWe have tested our method on different kinds of data.We have segmented urban data infigure6from our Mobile Mapping System(MMS)described in[11].The mobile sys-tem generates10k pts/s with a density of50pts/m2and very noisy data(σ=0.3m).For this point cloud,we want to de-tect building facades.We have chosen area min=10m2, d=1m to have large connected components andγ=0.3m to cope with the noise.We have tested our method on point cloud from the Trim-ble VX scanner infigure7.It is a point cloud of size40k points with only20pts/m2with less noise because it is a fixed scanner(σ=0.2m).In that case,we also wanted to detect building facades and keep the same parameters ex-ceptγ=0.2m because we had less noise.We see infig-ure7that we have detected two facades.By setting a larger voxel size d value like d=10m,we detect only one plane. We choose d like area min andγaccording to the desired segmentation and to the level of detail we want to extract from the point cloud.We also tested our algorithm on the point cloud from the LEICA Cyrax scanner infigure8.This point cloud has been taken from AIM@SHAPE repository[1].It is a very dense point cloud from multiplefixed position of scanner with about400pts/m2and very little noise(σ=0.02m). In this case,we wanted to detect all the little planes to model the church in planar regions.That is why we have chosen d=0.2m,area min=1m2andγ=0.02m.Infigures6,7and8,we have,on the left,input point cloud and on the right,we only keep points detected in a plane(planes are in random colors).The red points in thesefigures are seed plane points.We can see in thesefig-ures that planes are very well detected even with high noise. Table3show the information on point clouds,results with number of planes detected and duration of the algorithm.The time includes the computation of the FWPF normalsof the point cloud.We can see in table3that our algo-rithm performs linearly in time with respect to the numberof points.The choice of parameters will have little influence on time computing.The computation time is about one mil-lisecond per point whatever the size of the point cloud(we used a PC with QuadCore Q9300and2Go of RAM).The algorithm has been implented using only one thread andin-core processing.Our goal is to compare the improve-ment of plane detection between classical region growing and our region growing with better normals for more ac-curate planes and voxel growing for faster detection.Our method seems to be compatible with out-of-core implemen-tation like described in[24]or in[15].MMS Street VX Street Church Size(points)398k42k7.6MMean Density50pts/m220pts/m2400pts/m2 Number of Planes202142Total Duration452s33s6900sTime/point 1ms 1ms 1msTable3.Results on different data.5.ConclusionIn this article,we have proposed a new method of plane detection that is fast and accurate even in presence of noise. We demonstrate its efficiency with different kinds of data and its speed in large data sets with millions of points.Our voxel growing method has a complexity of O(N)and it is able to detect large and small planes in very large data sets and can extract them directly in connected components.Figure 4.Ground truth,Our Segmentation without and with filterednormals.Figure 6.Planes detection in street point cloud generated by MMS (d =1m,area min =10m 2,γ=0.3m ).References[1]Aim@shape repository /.6[2]Octree class template /code/octree.html.4[3] A.Bab-Hadiashar and N.Gheissari.Range image segmen-tation using surface selection criterion.2006.IEEE Trans-actions on Image Processing.1[4]J.Bauer,K.Karner,K.Schindler,A.Klaus,and C.Zach.Segmentation of building models from dense 3d point-clouds.2003.Workshop of the Austrian Association for Pattern Recognition.1[5]H.Boulaassal,ndes,P.Grussenmeyer,and F.Tarsha-Kurdi.Automatic segmentation of building facades using terrestrial laser data.2007.ISPRS Workshop on Laser Scan-ning.1[6] C.C.Chen and I.Stamos.Range image segmentationfor modeling and object detection in urban scenes.2007.3DIM2007.1[7]T.K.Dey,G.Li,and J.Sun.Normal estimation for pointclouds:A comparison study for a voronoi based method.2005.Eurographics on Symposium on Point-Based Graph-ics.3[8]J.R.Diebel,S.Thrun,and M.Brunig.A bayesian methodfor probable surface reconstruction and decimation.2006.ACM Transactions on Graphics (TOG).1[9]M.A.Fischler and R.C.Bolles.Random sample consen-sus:A paradigm for model fitting with applications to image analysis and automated munications of the ACM.1,2[10]P.F.U.Gotardo,O.R.P.Bellon,and L.Silva.Range imagesegmentation by surface extraction using an improved robust estimator.2003.Proceedings of Computer Vision and Pat-tern Recognition.1,5[11] F.Goulette,F.Nashashibi,I.Abuhadrous,S.Ammoun,andurgeau.An integrated on-board laser range sensing sys-tem for on-the-way city and road modelling.2007.Interna-tional Archives of the Photogrammetry,Remote Sensing and Spacial Information Sciences.6[12] A.Hoover,G.Jean-Baptiste,and al.An experimental com-parison of range image segmentation algorithms.1996.IEEE Transactions on Pattern Analysis and Machine Intelligence.5[13]H.Hoppe,T.DeRose,T.Duchamp,J.McDonald,andW.Stuetzle.Surface reconstruction from unorganized points.1992.International Conference on Computer Graphics and Interactive Techniques.2[14]P.Hough.Method and means for recognizing complex pat-terns.1962.In US Patent.1[15]M.Isenburg,P.Lindstrom,S.Gumhold,and J.Snoeyink.Large mesh simplification using processing sequences.2003.。

卡尔曼滤波英文版The Kalman Filter: A Powerful Tool for Optimal Estimation and PredictionThe Kalman filter is a mathematical algorithm that provides an efficient computational means to estimate the state of a dynamic system from a series of measurements. Developed by Rudolf E. Kalman in 1960, this powerful tool has found widespread applications in various fields, including aerospace engineering, robotics, navigation, and signal processing. The Kalman filter is particularly useful in situations where the system being observed is subject to random disturbances or the measurements contain noise.At the heart of the Kalman filter is the concept of state estimation. In a dynamic system, the state represents the essential information needed to describe the system's behavior at a particular point in time. This state may include variables such as position, velocity, acceleration, or any other relevant parameters. The Kalman filter uses a recursive algorithm to estimate the system's state based on a series of noisy measurements, providing an optimal estimate thatminimizes the mean square error.The Kalman filter operates in two distinct phases: the prediction phase and the update phase. In the prediction phase, the algorithm uses the system's dynamics and the previous state estimate to predict the current state. This prediction is then combined with the current measurement in the update phase, where the Kalman filter calculates a weighted average of the predicted state and the measured state. The weighting factor, known as the Kalman gain, is determined based on the relative uncertainties of the prediction and the measurement.One of the key advantages of the Kalman filter is its ability to handle uncertainty and noise in the system and measurements. By continuously updating the state estimate based on the available measurements, the Kalman filter can effectively filter out the noise and provide a smooth, accurate estimate of the system's state. This makes the Kalman filter particularly useful in applications where the measurements are unreliable or subject to various sources of noise, such as sensor errors, environmental disturbances, or measurement delays.Another important aspect of the Kalman filter is its recursive nature. Instead of storing and processing all past measurements, the Kalman filter only requires the current measurement and the previous stateestimate to compute the current state estimate. This makes the algorithm computationally efficient and well-suited for real-time applications, where the system's state needs to be estimated and updated continuously.The versatility of the Kalman filter has led to its widespread adoption in a variety of applications. In the field of aerospace engineering, the Kalman filter is extensively used for aircraft and spacecraft navigation, guidance, and control. By combining measurements from multiple sensors, such as GPS, inertial measurement units, and radar, the Kalman filter can provide a robust and accurate estimate of the vehicle's position, orientation, and velocity.In the field of robotics, the Kalman filter is used to track the position and orientation of mobile robots, enabling them to navigate through complex environments and perform tasks with high precision. In signal processing, the Kalman filter is employed to remove noise and distortion from various types of signals, such as audio, video, and communication signals, improving the quality and clarity of the output.Beyond these traditional applications, the Kalman filter has also found use in emerging areas like autonomous vehicles, where it plays a crucial role in fusing data from multiple sensors (e.g., cameras, lidar, radar) to provide a comprehensive understanding of the vehicle'ssurroundings and enable accurate localization, mapping, and decision-making.The widespread adoption of the Kalman filter is a testament to its mathematical elegance and practical effectiveness. The algorithm's ability to provide optimal state estimates in the presence of uncertainty and noise has made it an indispensable tool in a wide range of fields, from aerospace to robotics and beyond.As technology continues to advance, the Kalman filter is likely to remain a fundamental component in many complex systems, enabling more accurate, reliable, and efficient solutions to a myriad of real-world problems. Its ongoing evolution and integration with emerging technologies, such as deep learning and sensor fusion, are likely to unlock even greater capabilities and applications in the years to come.。

2019年第38卷第4期传感器与微系统（Transducer and Microsystem Technologies）DOI：10．13873/J．1000—9787（2019）04—0051—04一种抵御虫洞攻击的安全定位方法段正飞，冯军焕（西南交通大学信息科学与技术学院，四川成都611756）摘要：针对无线传感器网络中，虫洞攻击对节点定位过程的影响，提出了一种改进的抵御虫洞攻击的距离矢量跳（DV-Hop）安全定位算法。

通过改进建立冲突集的方法，选出最合适的信标节点广播睡眠信息，尽可能减少进入睡眠状态的节点的数量，进一步提高了虫洞检测成功率和定位精度。

将所提算法与基于标记来抵御虫洞攻击的DV-Hop（LBDV）算法进行比较，仿真结果表明：提出的安全定位算法性能优于LB-DV算法。

关键词：无线传感器网络；安全定位；虫洞攻击；距离矢量跳中图分类号：TP393文献标识码：A文章编号：1000—9787（2019）04—0051—04A security localization method for resisting wormhole attacksDUAN Zheng-fei，FENG Jun-huan（School of Information Science and Technology，Southwest Jiaotong University，Chengdu611756，China）Abstract：Aiming at the impact of wormhole attack on node localization in wireless sensor networks（WSNs），animproved DV-Hop security localization algorithm is proposed to resist wormhole attack．By improving the method ofestablishing conflict set，the most appropriate beacon nodes are selected to broadcast sleep information，as far aspossible to reduce the number of nodes go into sleep state，to further improve the success rate of wormholedetection and localization precision．The algorithm is compared with the label-based DV-Hop localization againstwormhole attacks（LBDV）algorithm．The simulation results show that the performance of the security localizationalgorithm described in this paper is better than that of the LBDV algorithm．Keywords：wireless sensor networks（WSNs）；secure localization；wormhole attack；distance vector（DV）-Hop0引言无线传感器网络的安全性引起了国内外学者的广泛研究［1］。

第19卷 第6期 太赫兹科学与电子信息学报Vo1.19，No.62021年12月 Journal of Terahertz Science and Electronic Information Technology Dec.，2021文章编号：2095-4980(2021)06-1008-06基于强跟踪五阶容积卡尔曼滤波的眼睛跟踪算法殷晓春1a，蔡晨晓2，李建林1b(1.南京信息职业技术学院 a.人工智能学院；b.网络与通信学院，江苏南京 210023；2.南京理工大学自动化学院，江苏南京 210094)摘 要：非侵入式眼睛跟踪在许多基于视觉的人机交互应用中扮演十分重要的角色，但由于眼睛运动的强非线性，如何确保眼睛跟踪过程中对外界干扰的鲁棒性以及跟踪精确度是其应用的关键问题。

为提高眼睛跟踪的鲁棒性和精确度，提出强跟踪五阶容积卡尔曼滤波算法(ST-5thCKF)，将强跟踪滤波(STF)次优渐消因子引入具有接近最少容积采样点且保持五阶滤波精确度的五阶容积卡尔曼滤波(5thCKF)，获取5thCKF对强非线性良好滤波精确度同时具备STF对外界干扰的鲁棒性。

真实条件下的实验结果验证了所提算法在眼睛跟踪中的有效性。

关键词：眼睛跟踪；强跟踪滤波；五阶容积卡尔曼滤波；强跟踪五阶容积卡尔曼滤波中图分类号：TP391.4 文献标志码：A doi：10.11805/TKYDA2020427Eye tracking algorithm based on strong tracking fifth-degree cubature Kalman filterYIN Xiaochun1a，CAI Chenxiao2，LI Jianlin1b(1a.Institute of Artificial Intelligence；1b.School of Network and Communication，Nanjing Vocational College of Information Technology，Nanjing Jiangsu 210023，China；. All Rights Reserved.2.School of Automation，Nanjing University of Science and Technology，Nanjing Jiangsu 210094，China)Abstract：Non-intrusive eye tracking plays an important role in many vision-based human computer interaction applications. How to ensure the robustness of external interference and tracking precisionduring eye tracking is the key problem to its applications owing to the strong nonlinearity of eye motion.To improve the robustness and precision of eye tracking, the Strong Tracking fifth-degree CubatureKalman Filter(ST-5thCKF) algorithm is proposed. The algorithm introduces the suboptimal fading factor ofStrong Tracking Filter(STF) into fifth-degree Cubature Kalman Filter(5thCKF) which almost has the leastcubature sampling points while maintaining the fifth-degree filtering accuracy. The proposed algorithmbears the high filtering precision to strong nonlinearity of 5thCKF, as well as the robustness to externalinterference of STF. The experimental results under practical conditions show the validity of the proposedalgorithm in eye tracking.Keywords：eye tracking；Strong Tracking Filter(STF)；fifth-degree Cubature Kalman Filter(5thCKF)；Strong Tracking fifth-degree Cubature Kalman Filter(ST-5thCKF)眼睛跟踪对于提高日常人机交互的质量具有重要作用，在司机疲劳驾驶检测[1-4]、虚拟现实系统(Virtual Reality System，VR)的图像扫描、飞行人员行为检测[5]、眼睛与电脑交互[6]等领域得到广泛应用。

时延估计算法的方法很多，广义互相关函数法(Gee, Genear I i zedeross-ocerrat Inin)运用最为广泛"广义互相关法通过求两信号之间的互功率谱，并在频域内给予一定的加权，来抑制噪声和反射的影响，再反变换到时域，得到两信号之间的互相关函数"其峰值位置，即两信号之间的相对吋延45IH, 6],时延估计过程如图1 一7所示”设h. (n), h2 (n)分别为声源信号s (n)到两麦克风的冲激响应，則麦克风接收到的信号为：Xi (n) =hi (n) 0S (n) +ni (n) (1. 1)x2 (n) =h2 (n) 0 s (n) +n2 (n) (1.2)佈计结果结基于子空间的定位技术来源于现代高分辨率谱估计技术。

子空间技术是阵列信号处理技术中研究最多、应用最广、最基本也是最重要的技术之一。

该类声源定位技术是利用接收信号相关矩阵的空间谱，求解麦克风间的相关矩阵来确定方向角, 从而进一步确定声源位置。

子空间类方法主要分两类，一类是利用阵列自相关矩阵主特征向量(即信号子空间)的主分量方法，如AR参数模型主分量法，BT主分量法等；另一类方法是以信号子空间和噪声子空间的正交性原理为基础，利用组成噪声子空间的特征向量来进行谱估计，这类算法主要有多重信号分类法(MUSIC), Johnson 法,最小范数(Mini-Norm)法,MUSIC 根(Root-MUSIC)法, 旋转不变信号参数估计(ESPRIT)法，等等。

在实际中，基于子空间的定位技术的空间谱的相关矩阵是未知的，必须从观测信号中来估计，需要在一定时间间隔内把所有信号平均来得到，同时要求接收信号处于声源、噪声、估计参数固定不变的环境和有足够多的信号平均值。

即便满足这此条件，该算法也不如传统的波束形成方法对声源和麦克风模型误差的鲁棒性好。

目前定位问题所涉及算法都是研究远场的线性阵列情况。

基于子空间的定位技术是通过时间平均来估计信号之间的相关矩阵，需要信号是平稳过程，估计参数固定不变，而语音信号是一个短时平稳过程，往往不能满足这个条件。

移动机器人的在线实时定位研究徐德谭民（北京中科院自动化研究所复杂系统与智能科学实验室）（电子邮箱：xude@）摘要对推算定位法进行了研究,提出了一种改进方案.通过对移动机器人运动轨迹与状态的分析,导出了一类移动机器人的基于轨迹的运动学新模型.利用移动机器人三个轮子的里程信息和导向轮的转角信息,通过信息模糊融合获得转弯半径和转角,再利用运动学模型获得机器人的位置和方向.将这种改进的推算定位法与主动灯塔法相结合,提出了一种用于室内移动机器人的定位方法.仿真结果表明,该方法具有实时性好、精度高、成本低、鲁棒性好等特点,并适用于不平整地面。

关键词：移动机器人，定位，运动学，模糊融合1绪论定位是机器人的基础，因此，精准的实时定位是提高机器人性能的关键因素，也是一个机器人研究的热点。

定位的方法可以分为两种：相对位置和绝对位置。

相对位置定位法又叫推算定位，可以细分为测距法和惯性导航。

绝对位置定位法可以使用全球定位系统、基站定位、罗盘导航、地标导航和地图匹配等方式实现。

它们各有特色，但是只有推算定位，基站定位，地标导航和地图匹配可以适用于室内移动机器人。

因为测距法在实时系统的实际应用中有很高的性价比所以成了推算定位最常用的方法。

通常的做法是将其他的绝对定位方式得到的结果通过信息融合的方法得出更好的定位效果。

这种方式能在小范围和大范围内都有很高的定位精度。

在1996年，Lazea用运动和反向运动的模型研究机器人。

1997年，Chong 和Kleeman 提出了一种利用统计误差来判断定位误差的推算定位机器人模型。

同在1996年，Borenstein 和Feng 提出一种系统误差验证法。

这些方法都能很好地用来实时定位，但是在不平整的场地它们的性能都会大打折扣。

同一年，Burgard提出另外一种解决方法，通过将摄像头得到的图像跟全局地图进行比较将可以得到绝对坐标和方向估计。

1999年，Roy和Thrun 给出一种利用外部地图的统计学方法用以校正机器人的位置。