Improvement of iterative closest point with edges of projected image

Background There are many regularly shaped objects in artificial environments.It is difficult to distinguish the poses of these objects when only geometric information is used.With the development of sensor technologies,inclusion of other information can be used to solve this problem.Methods We propose an algorithm to register point clouds by integrating color information.The key idea of the algorithm is to jointly optimize the dense and edge terms.The dense term was built in a manner similar to that of the iterative closest point algorithm.To build the edge term,we extracted the edges of the images obtained by projecting point clouds.The edge term prevents the point clouds from sliding during registration.We used this loosely coupled method to fuse geometric and color information.Results The results of the experiments showed that the edge image approach improves precision,and the algorithm is robust.

Non-cooperative target pose estimation based on improved iterative closest point algorithm

For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes a new iterative closest point(ICP)algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation.As interference points in space have not yet been extensively studied,we classify them into two broad categories,far interference points and near interference points.For the former,the statistical outlier elimination algorithm is employed.For the latter,the Gaussian distributed weights,simultaneously valuing with the variation of the Euclidean distance from each point to the centroid,are commingled to the traditional ICP algorithm.In each iteration,the weight matrix W in connection with the overall localisation is obtained,and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose.Finally,the experiments are implemented by shooting the satellite model and setting the position of interference points.The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation.When the interference point number reaches about 700,the average error of angle is superior to 0.88°.

A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization （PSO） with iterative closest point （ICP） algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.

Natural forest ALS-TLS point cloud data registration without control points

Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.

Global Fine Registration of Point Cloud in LiDAR SLAM Based on Pose Graph

The laser scanning system based on Simultaneous Localization and Mapping(SLAM)technology has the advantages of low cost,high precision and high efficiency.It has drawn wide attention in the field of surveying and mapping in recent years.Although real-time data acquisition can be achieved using SLAM technology,the precision of the data can’t be ensured,and inconsistency exists in the acquired point cloud.In order to improve the precision of the point cloud obtained by this kind of system,this paper presents a hierarchical point cloud global optimization algorithm.Firstly,the“point-to-plane”iterative closest point(ICP)algorithm is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system.Then a pose graph is constructed to optimize the trajectory.Finally,the optimized trajectory is used to refine the point cloud.The computational efficiency is improved by decomposing the optimization process into two levels,i.e.local level and global level.The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50%after optimization,and the internal inconsistency is effectively eliminated.

Multi-View Point Clouds Registration without Feature Extraction for Use in Reverse Engineering of CAD Models

For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with point-normal method, further refinement is achieved by using an improved iterative closest point (ICP) algorithm. Compared with other methods used for mult-view registration, this approach is automatic because no geometric feature, such as line, plane or sphere needs to be extracted from the original point cloud manually. A good initial alignment can be acquired automatically and the registration accuracy and efficiency is proven better than the normal point-point ICP algorithm both experimentally and theoretically.

Easy to Calibrate:Marker-Less Calibration of Multiview Azure Kinect

Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously,Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated frommultiple Kinect devices,including themarker detection method.In this study,we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection,which directly affects calibration accuracy;it offers superior userfriendliness,efficiency,and accuracy.Further,we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration;however,Azure Kinect,the latest version of Kinect,requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.

A novel algorithm for SLAM in dynamic environments using landscape theory of aggregation

To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.

Multi-view ladar data registration in obscure environment

Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.

3D face registration based on principal axis analysis and labeled regions orientation

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the multi-pose models to the standard frontal model＇s reference frame using the principal axis analysis algorithm.Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics.These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration.Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

Surface registration algorithm for rapid detection of surface thermal deformation of paraboloid antennas

In order to obtain and master the surface thermal deformation of paraboloid antennas,a fast iterative closest point( FICP) algorithm based on design coordinate guidance is proposed,which can satisfy the demands of rapid detection for surface thermal deformation. Firstly,the basic principle of the ICP algorithm for registration of a free surface is given,and the shortcomings of the ICP algorithm in the registration of surface are analysed,such as its complex computation,long calculation time,low efficiency,and relatively strict initial registration position. Then an improved FICP algorithm based on design coordinate guidance is proposed. Finally,the FICP algorithm is applied to the fast registration test for the surface thermal deformation of a paraboloid antenna. Results indicate that the approach offers better performance with regard to fast surface registration and the algorithm is more simple,efficient,and easily realized in practical engineering application.

Indoor 3D Reconstruction Using Camera, IMU and Ultrasonic Sensors

The recent advances in sensing and display technologies have been transforming our living environments drastically. In this paper, a new technique is introduced to accurately reconstruct indoor environments in three-dimensions using a mobile platform. The system incorporates 4 ultrasonic sensors scanner system, an HD web camera as well as an inertial measurement unit (IMU). The whole platform is mountable on mobile facilities, such as a wheelchair. The proposed mapping approach took advantage of the precision of the 3D point clouds produced by the ultrasonic sensors system despite their scarcity to help build a more definite 3D scene. Using a robust iterative algorithm, it combined the structure from motion generated 3D point clouds with the ultrasonic sensors and IMU generated 3D point clouds to derive a much more precise point cloud using the depth measurements from the ultrasonic sensors. Because of their ability to recognize features of objects in the targeted scene, the ultrasonic generated point clouds performed feature extraction on the consecutive point cloud to ensure a perfect alignment. The range measured by ultrasonic sensors contributed to the depth correction of the generated 3D images (the 3D scenes). Experiments revealed that the system generated not only dense but precise 3D maps of the environments. The results showed that the designed 3D modeling platform is able to help in assistive living environment for self-navigation, obstacle alert, and other driving assisting tasks.

A Tracking Registration Method for Augmented Reality Based on Multi-modal Template Matching and Point Clouds

In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking registration method based on multimodal template matching and point clouds is proposed.The method first adapts the linear parallel multi-modal LineMod template matching method with scale invariance to identify the texture-less target and obtain the reference image as the key frame that is most similar to the current perspective.Then,we can obtain the initial pose of the camera and solve the problem of re-initialization because of tracking registration interruption.A point cloud-based method is used to calculate the precise pose of the camera in real time.In order to solve the problem that the traditional iterative closest point(ICP)algorithm cannot meet the real-time requirements of the system,Kdtree(k-dimensional tree)is used under the graphics processing unit(GPU)to replace the part of finding the nearest points in the original ICP algorithm to improve the speed of tracking registration.At the same time,the random sample consensus(RANSAC)algorithm is used to remove the error point pairs to improve the accuracy of the algorithm.The results show that the proposed tracking registration method has good real-time performance and robustness.

一种基于全等三角形的点云自动配准方法

点云配准是点云数据处理中比较关键的步骤,直接影响处理结果。经典的迭代最近点(iterative closest point,ICP)算法需要目标点云与源点云之间有良好的初始姿态,否则会遇到局部最优等问题。因此,提出了一种基于全等三角形的点云自动配准方法。该方法通过边长相等和面积相等来构造全等三角形,找到源点云与目标点云的对应点,建立源点云与目标点云的对应关系和转换参数最优估计,完成粗配准,再结合ICP算法进行精配准,实现点云的自动配准。结果表明,与四点一致集(4-points congruent sets,4PCS)配准算法和ICP算法的结合算法相比,所提算法能有效改善ICP算法对初值依赖的问题,并且配准精度有一定提升。

A Method for Range Image Registration via Neural Network and ICP Algorithm

In this paper,a new integrated registration algorithm based on neural network and ICP（iterative closest point） algorithm is presented.A coarse registration process is implemented with neural network,and then further optimized by ICP algorithm.The corresponding point pairs are found according to the target points＇ curvature and color information.Mahalanobis distance,which reflects the scattering degree of point data,is employed to define the closest distance and the closest points.The results of the experiment show that our algorithm has better feasibility,validity and efficiency than the traditional ICP method.

Underwater map-matching aided inertial navigation system based on multi-geophysical information

In order to achieve long-term covert precise navigation for an underwater vehicle,the shortcomings of various underwater navigation methods used are analyzed.Given the low navigation precision of underwater mapmatching aided inertial navigation based on singlegeophysical information,a model of an underwater mapmatching aided inertial navigation system based on multigeophysical information(gravity,topography and geomagnetism)is put forward,and the key technologies of map-matching based on multi-geophysical information are analyzed.Iterative closest contour point(ICCP)mapmatching algorithm and data fusion based on Dempster-Shafer(D-S)evidence theory are applied to navigation simulation.Simulation results show that accumulation of errors with increasing of time and distance are restrained and fusion of multi-map-matching is superior to any single-map-matching,which can effectively determine the best match of underwater vehicle position and improve the accuracy of underwater vehicle navigation.