Parallel 3D ICP Based on Conditionally Constrained Corresponding Points and Applications

Point cloud registration is a central theme in computer vision, with alignment algorithms continuously improving for greater robustness. Commonly used methods evaluate Euclidean distances between point clouds and minimize an objective function, such as Root Mean Square Error (RMSE). However, these approaches are most effective when the point clouds are well-prealigned and issues such as differences in density, noise, holes, and limited overlap can compromise the results. Traditional methods, such as Iterative Closest Point (ICP), require choosing one point cloud as fixed, since Euclidean distances lack commutativity. When only one point cloud has issues, adjustments can be made, but in real scenarios, both point clouds may be affected, often necessitating preprocessing. The authors introduce a novel differential entropy-based metric, designed to serve as the objective function within an optimization framework for fine rigid pairwise 3D point cloud registration, denoted as Iterative Differential Entropy Minimization (IDEM). This metric does not depend on the choice of a fixed point cloud and, during transformations, reveals a clear minimum corresponding to the best alignment. Multiple case studies are conducted, and the results are compared with those obtained using RMSE, Chamfer distance, and Hausdorff distance. The proposed metric proves effective even with density differences, noise, holes, and partial overlap, where RMSE does not always yield optimal alignment.

Framework for automated registration of UAV and UGV point clouds using local features in images

Point set augmentation through fitting for enhanced ICP registration of point clouds in multisensor coordinate metrology

This paper presents a method to distinguish between a manipulator and its surroundings using a depth sensor. The depth sensor used is Kinect. First Kinect calibration is addressed. Then coordinate calibration between Kinect and the manipulator are solved using iterative least square (ILS) algorithm. At this point, to delete the robot from the scene and keep only the surrounding surface, the accuracy of homogeneous transformation acquired from ILS is inadequate. We further focus on a matching method between the manipulator's model and point cloud, to use iterative closest point (ICP) algorithm. ICP enhances the accuracy for a great deal. Experiment shows that this comprehensive method is practical and robust. It can be used in dynamic environment as well.

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

Pantograph is a power collector to get current from the overhead catenary for electric train engine. When the train moves forward, the high frequency friction between pantograph and catenary gives rise to the wear of the carbon strips. In this paper, we propose an efficient stereo-based method to non-contact monitor the pantograph wearing. Firstly, a modified SGM-based stereo matching algorithm is raised to overcome the complicated illumination in the actual environment. Secondly, the point cloud segmentation using Radius Filter and Density-based Spatial Clustering of Applications with Noise (RF-DBSCAN) is carried out to eliminate noisy points caused by mismatching and extract the carbon strips from background. Finally, for the purpose of wearing detection, the processed point cloud is aligned with stand CAD model by the proposed coarse-to-fine iterative closest point (CF-ICP) method. The experimental data which is obtained from a real train maintenance depot reveals that root mean square (RMS) of the wearing inspection is less than 4 mm, which outperform than other classic methods

Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an effective procedure for facial reconstruction as well as facial recognition via using a depth sensor. For each testing candidate, the depth camera acquires a multi-view of its 3D point clouds. The point cloud sets are stitched for 3D model reconstruction by using the iterative closest point (ICP). Then, a segmentation procedure is designed to separate the model set into a body part and head part. Based on the segmented 3D face point clouds, certain facial features are then extracted for recognition scoring. Taking a single shot from the depth sensor, the point cloud data is going to register with other 3D face models to determine which is the best candidate the data belongs to. By using the proposed feature-based 3D facial similarity score algorithm, which composes of normal, curvature, and registration similarities between different point clouds, the person can be labeled correctly even in a dark environment. The proposed method is suitable for smart devices such as smart phones and smart pads with tiny depth camera equipped. Experiments with real-world data show that the proposed method is able to reconstruct denser models and achieve point cloud-based 3D face recognition.

Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

Accurate estimation of tree position, diameter at breast height (DBH), and tree height measurements is an important task in forest inventory. Mobile Laser Scanning (MLS) is an important solution. However, the poor global navigation satellite system (GNSS) coverage under the canopy makes the MLS system unable to provide globally-consistent point cloud data, and thus, it cannot accurately estimate the forest attributes. SLAM could be an alternative for solutions dependent on GNSS. In this paper, a mobile phone with RGB-D SLAM was used to estimate tree position, DBH, and tree height in real-time. The main aims of this paper include (1) designing an algorithm to estimate the DBH and position of the tree using the point cloud from the time-of-flight (TOF) camera and camera pose; (2) designing an algorithm to measure tree height using the perspective projection principle of a camera and the camera pose; and (3) showing the measurement results to the observer using augmented reality (AR) technology to allow the observer to intuitively judge the accuracy of the measurement results and re-estimate the measurement results if needed. The device was tested in nine square plots with 12 m sides. The tree position estimations were unbiased and had a root mean square error (RMSE) of 0.12 m in both the x-axis and y-axis directions; the DBH estimations had a 0.33 cm (1.78%) BIAS and a 1.26 cm (6.39%) root mean square error (RMSE); the tree height estimations had a 0.15 m (1.08%) BIAS and a 1.11 m (7.43%) RMSE. The results showed that the mobile phone with RGB-D SLAM is a potential tool for obtaining accurate measurements of tree position, DBH, and tree height.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

Fine registration of 3D point clouds fusing structural and photometric information using an RGB-D camera

We present an algorithm that simultaneously identifies the structure and learns the kinematic model of an articulated body from point cloud data and joint angle data with minimal prior assumptions. The robot arm is represented by a point cloud. The structure is identified by segmenting the point cloud to subsets corresponding to different links according to a minimum matching error criterion that includes spatial continuity. The kinematic model is represented as a set of rigid transformations, which are learned by a stochastic gradient decent-based iterative closest point (ICP) algorithm. The learned transformations are then used to infer the structural relationship between segments. We validate the experiment on both synthetic data and data acquired by a Kinect depth sensor viewing a robot arm moving three of its joints. Our results demonstrate that point cloud can be correctly segmented into different rigid parts; the rigid transformations associated with each part can be learned and the kinematic structure can be correctly inferred. Our proposed approach makes a step toward a unified framework for robot self-discovery and learning forward kinematic models purely with data from depth sensors and joint angle encoders.

Among emerging 3D scanning and imaging techniques that are commercially available, simultaneous localization and mapping (SLAM) is being substantially studied to generate 2D/3D maps of an unknown environment while reliably keeping track of the user’s pinpoint locations. Its ubiquitous mobility has demonstrated great mapping capabilities for infrastructures where vertical information may frequently be occluded using unmanned aircraft system (UAS) structure from-motion (SfM) photogrammetry. In addition, indoor mapping with terrestrial laser scanning can be a cumbersome task due to possible multiple scan locations. Intending to provide a cohesive 3D model by fusing point clouds collected via aerial SfM photogrammetry, terrestrial laser scanning (TLS), and SLAM, the purpose of this work is to assess the performance of SLAM point cloud generated by a proprietary mobile backpack laser scanner (BLS). Considering maximum scanning range and information integration strategy as variables, the point clouds generated by the BLS were evaluated against SfM and TLS datasets in terms of the internal consistency as well as external accuracy. TLS, SfM and SAM data collection efforts were made in a typical university campus environment. For the internal consistency, the SLAM-based point cloud with a maximum scanning range of 70 m presented a root mean square error (RMSE) of 2 mm. The SLAM+GNSS-based point cloud presented the lowest internal precision of RMSE = 0.861 m. The SLAM+GNSS 70 point cloud after a fine adjustment of misalignment presented the highest vertical accuracy with an RMSE = 0.069 m, while the point cloud generated from SfM photogrammetry presented RMSE = 0.297 m. The BLS was able to generate point cloud with an accuracy similar to GNSS-RTK surveying and it can be considered as a viable solution for indoor and outdoor mapping applications.

This paper introduces an improved approach for the volume data registration of human retina. Volume data registration refers to calculating out a near-optimal transformation between two volumes with overlapping region and stitching them together. Iterative closest point (ICP) algorithm is a registration method that deals with registration between points. Classical ICP is time consuming and often traps in local minimum when the overlapping region is not big enough. Optical Coherence Tomography (OCT) volume data are several separate, partially overlapping tiles. To stitch them together is a technology in computer aided diagnosis. In this paper, a new 3D registration algorithm based on improved ICP is presented. First, the Canny edge detector is applied to generate the point cloud set of OCT images. After the detection step, an initial registration method based on the feature points of the point cloud is proposed to determine an initial transformation matrix by using singular value decomposition (SVD) method. Then, an improved ICP method is presented to accomplish fine registration. Corresponding point in the point cloud is weighted to reduce the iteration times of ICP algorithm. Finally, M-estimation is used as the objective function to decrease the impact of outliers. This registration algorithm is used to process human retinal OCT volume pairs that contain an overlapping region of 75ź?ź500ź?ź375 voxels approximately. Then a comparative experiment is conducted on some public-available datasets. The experimental results show that the proposed method outperforms the classical method.