Planar Constraint Research Articles

A Self-Calibration Method for Robot End-Effector Using Spherical Constraints

A self-calibration method utilizing spherical constraints is proposed for calibration of robot end-effectors. The method establishes a mathematical model to account for both the geometric errors of the robot and the deformation errors of the end-effector. A nonlinear least-squares parameter identification technique based on spherical constraints is employed to achieve autonomous calibration of the end-effector. Contrasted with methodologies relying on point plane or distance constraints, this novel technique delivers superior positioning accuracy, streamlined operational procedures and enhanced efficiency. Both simulation and experimental validation confirm that the self-calibration method using spherical constraints improves the positioning accuracy of the robot end-effector from 3 mm to 0.3 mm, showing the effectiveness of the method.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

Neural 3D Scene Reconstruction With Indoor Planar Priors.

This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planar constraints into the depth map estimation in multi-view stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multi-view consistency. In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. Specifically, we use an MLP network to represent the signed distance function as the scene geometry. Based on the Manhattan-world assumption and the Atlanta-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network. To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. Experiments on ScanNet and 7-Scenes datasets show that the proposed method outperforms previous methods by a large margin on 3D reconstruction quality.

A lateral approach allows accurate and stable total elbow replacement in dogs.

Evaluate whether total elbow replacement (TER) through a lateral approach is accurate and stable. 12 skeletally mature large-breed dog cadavers were used. Limb alignment, elbow joint motion, and collateral ligament laxity were evaluated preoperatively. The order of surgery (left or right) and the approach (lateral or medial) were randomly selected for TER in each dog. The other approach was used in the contralateral elbow. Intraoperative technical difficulties, duration of surgery, and anatomic complications were recorded. Limb alignment, elbow joint motion, collateral ligament laxity, and prosthetic component alignment were evaluated after surgery. Data were collected from June 11 to 15, 2023. The duration of surgery using a lateral or medial approach did not differ (P = .499). Anatomic complications were not observed. The lateral approach resulted in 8° more elbow extension (P = .003), 1.58° less lateral collateral ligament constraint (P = .033), 2.80° less medial collateral ligament constraint (P = .002), 4.38° less frontal plane constraint (P = .004), 8° greater humeral component inclination (P = .033), and 5.6° greater radioulnar component varus (P = .001) than the medial approach. Varus of the radius, mechanical axis deviation, limb supination, elbow flexion, mediolateral humeral component and craniocaudal radioulnar component orientation did not differ among joints operated using a lateral or medial approach. In normal cadaveric elbows, a lateral approach for TER appears feasible, producing equivalent limb alignment, joint laxity, and joint motion to normal elbows and to TER placed using a medial approach. In dogs, TER can be performed using a lateral surgical approach.

External parameter calibration of laser scanner based on the combination of plane-control-based and plane-constraints-based methods

Accurate calibration of the external parameters of a laser scanner is crucial to ensure consistency in data representation from multiple sources. Plane features, known for their simplicity and ease of extraction, are widely used in external parameter calibration. The use of control planes with known plane parameters enables the simultaneous estimation of both translation and rotation parameters, while the use of planes with unknown parameters allows for efficient self-calibration. In this paper, we introduce a novel method that combines the strengths of both types of plane calibration methods to calibrate the external parameters of a laser scanner. The approach, with the help of a Gauss-Helmert adjustment model, integrates control and constraint planes to calibrate the external parameters. The method boasts the ease and accessibility offered by the plane constraint method, utilizing fewer control features while successfully estimating the external parameters of the laser scanner. To evaluate the feasibility of this method, two sets of 12 calibration experiments were conducted on the mobile measurement system platform. Notably, the residual Root Mean Square Errors (RMSE) obtained from these experiments were comparable to those achieved using traditional plane-control-based method. Furthermore, the external parameter correlation aligns with that obtained using the plane-control-based method, while the plane parameter correlation exhibits similarity to that obtained using the plane-constraint-based method. These results confirm that our method effectively minimizes the requirement for control features and enables more convenient calibration of the external parameters of the laser scanner.

DOT-SLAM: A Stereo Visual Simultaneous Localization and Mapping (SLAM) System with Dynamic Object Tracking Based on Graph Optimization.

Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization accuracy of the system decreases significantly. To mitigate this challenge, this paper unveils DOT-SLAM, a novel stereo visual SLAM system that integrates dynamic object tracking through graph optimization. By integrating dynamic object pose estimation into the SLAM system, the system can effectively utilize both foreground and background points for ego vehicle localization and obtain a static feature points map. To rectify the inaccuracies in depth estimation from stereo disparity directly on the foreground points of dynamic objects due to their self-similarity characteristics, a coarse-to-fine depth estimation method based on camera-road plane geometry is presented. This method uses rough depth to guide fine stereo matching, thereby obtaining the 3 dimensions (3D)spatial positions of feature points on dynamic objects. Subsequently, by establishing constraints on the dynamic object's pose using the road plane and non-holonomic constraints (NHCs) of the vehicle, reducing the initial pose uncertainty of dynamic objects leads to more accurate dynamic object initialization. Finally, by considering foreground points, background points, the local road plane, the ego vehicle pose, and dynamic object poses as optimization nodes, through the establishment and joint optimization of a nonlinear model based on graph optimization, accurate six degrees of freedom (DoFs) pose estimations are obtained for both the ego vehicle and dynamic objects. Experimental validation on the KITTI-360 dataset demonstrates that DOT-SLAM effectively utilizes features from the background and dynamic objects in the environment, resulting in more accurate vehicle trajectory estimation and a static environment map. Results obtained from a real-world dataset test reinforce the effectiveness.

POE-Based Error Modeling and Multiple Plane Constraint-Based Parameter Identification for the Kinematic Calibration of a 4-UPS/SPR Parallel External Fixator

Limb deformity is a common complaint in orthopedic surgery. Currently, gradual treatment using parallel external fixator (PEF) has become the preferred option for deformity correction. As the key medical apparatus with the special application properties of temporary assembly and direct utilization, the geometric errors generated during the manufacturing and assembly process of the PEF contribute to the post-correction residual and iatrogenic deformities. However, considering the complexity of clinical scenarios, there is a lack of an applicable solution to reduce the effect of the above error source of PEF. To overcome these problems, taking a 4-UPS/SPR PEF as the research object, this paper proposes a novel kinematic calibration method. First, the error model with the features of completeness, continuity, and minimality is first established based on the product of exponentials (POE) formula. Then, the identifiability analysis is conducted to eliminate the redundant error parameters and ensure the stability and accuracy of the iterative calculation in the process of parameter identification. Furthermore, a multiple plane constraint (MPC)-based parameter identification approach is introduced to the calibration task of the PEF to effectively reduce the difficulty of the pose information measurement in the current cost-effective constraint calibration method. Finally, the simulation and prototype experiments validate that the proposed calibration method (i.e., the combination of the POE-based error model and the MPC-based parameter identification approach) can accomplish the calibration tasks in clinical applications and meet the cost-effectiveness and efficiency requirements.

A Study on Disrupted Flight Recovery Based on Logic-Based Benders Decomposition Method

Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into a master problem and sub-problems. The algorithm uses MIP in the master problem to determine flight cancellations and aircraft replacements. In the sub-problems, MIP or CP is used to determine the departure time of delayed flights. Later, incorporating sectional constraints into the main problem and iterating until an optimal solution is obtained. Furthermore, the added cutting plane constraint in the iterations of the Benders decomposition algorithm are strengthened to eliminate more inferior solutions. By comparing the results of CPLEX, the Logic-Based Benders decomposition algorithm, and the enhanced Benders decomposition algorithm, it is verified that the improved Benders decomposition algorithm can solve large-scale examples more efficiently with a faster time and fewer iterations.

Simulation Study of LiDAR Calibration With Respect to Camera Using Point and Plane Constraints

Simulation Study of LiDAR Calibration With Respect to Camera Using Point and Plane Constraints

RGBD-Wheel SLAM System Considering Planar Motion Constraints

RGBD-Wheel SLAM System Considering Planar Motion Constraints

Musculoskeletal model degrees of Freedom: Frontal plane constraints are hindering our understanding of human movement

Induced acceleration analyses have expanded our understanding on the contributions of muscle forces to center of mass and segmental kinematics during a myriad of tasks. While these techniques have identified a subset of major muscle that contribute to locomotion, most analyses have included models with only one frontal plane degree of freedom (dof) actuated by the hip joint. The purpose of this study was to define the impact of including knee and subtalar joint frontal plane dof on model superposition accuracy and muscle specific contributions to mediolateral accelerations. Induced acceleration analyses were performed using OpenSim with the Lai model on a freely available dataset of one subject running at 4 m/s. Analyses were performed on four models (standard, with subtalar joint, with frontal plane knee, and combined frontal plane knee with subtalar) with the kinematic constraint and perturbation analyses. Root mean square error and correlations were computed against experimental kinematics. Adding frontal plane dofs improved mediolateral acceleration correlations on average by > 0.25 while only minimally impacting errors. The constraints method performed better than the perturbation method for mediolateral accelerations. Including frontal plane knee dof resulted in muscle and method specific responses. All muscles presented with a complete flip of polarity for constraint method, imparted by allowing the medial/lateral muscles to contribute according to their anatomical function. Only the gluteus medius flipped for the perturbation method. This study provides significant support for the inclusion of frontal plane knee and subtalar dof and the need for reevaluation of muscle contributions via induced acceleration.

A new iterative mixed integer linear programming based real time energy efficient management of AC-DC distribution networks

In the smart grid era, AC-DC distribution networks are evolving for integrating massive amounts of distributed energy resources, especially battery associated solar power generation units, into distribution networks in most energy efficient ways. Performing real time energy management for such networks is challenging due to presence of different power electronic converters, grid-edge devices and voltage controlling equipment. Existing studies either focused on developing offline day ahead methods, or shortsighted greedy strategies by neglecting the offline beneficial attributes in real time optimization frameworks. On the contrary, this article proposes a novel real time AC-DC distribution network energy management portfolio by merging load control and conservation voltage reduction techniques to achieve energy efficient operation of the network by regulating the operation of the controllable resources. Initially, the overall optimization problem is developed as a mixed integer non-convex programming by modelling the offline benefits as time coupled stochastic expressions in real time optimization platform. The non-convex expressions are later simplified by adopting different linearization techniques and the entire energy management framework is revised as a simple mixed integer linear programming (MILP). The original problem is decoupled for each time step by simplifying the time coupled expressions using virtual queues and Lyapunov functions. Additionally, a new iterative MILP algorithm is proposed to solve the revised energy management problem with less computation complexity and time. Unlike the previous solution methods, the proposed algorithm initializes the variables by their median values and handles the infeasibilities by adding tangential cutting plane constraints and penalty variable minimization objective. Demonstrating on 33-bus AC-DC distribution network, the superiority of the proposed real time energy management process and iterative MILP algorithm is established after comparing with standard energy management portfolios and existing solution methods.© 2017 Elsevier Inc. All rights reserved.

A Unified Fracture Model for X65 Pipeline Material Under Various Constraints Using the Extended Finite Element Method (XFEM)

Abstract It is generally accepted that the fracture strain is dependent on geometry/constraints in metals. However, the current available implementation of XFEM assumes a fixed fracture strain criterion independent of the constraints. The objective of this paper is to develop and implement a variable fracture strain criterion in XFEM that is capable of predicting a wide range of fracture conditions in X65 pipeline steels with various crack tip constraints. Various small-scale tests with different out of plane constraints obtained from the literature were simulated using the XFEM in ABAQUS software. For each test, the value of the maximum principal strain (Maxpe) as a fracture initiation criterion in the cohesive zone model (CZM) in XFEM framework was varied while keeping the fracture energy constant until the model was able to accurately replicate the reported experimental results. For each test, the crack tip constraints were characterized and the stress triaxialities and Lode angle parameters at the onset of fracture initiation were calculated from the models. The results allowed expressing the fracture strain as an explicit function of stress triaxiality which was then implemented in ABAQUS XFEM using UDMGINI subroutine. For the sake of comparison, the tests were simulated in finite element method (FEM) using a similar damage initiation model namely, the Johnson-Cook (J-C) model. A single model in XFEM was able to accurately replicate the experimental observations for all specimens and compared well to experiments in the way simulated by FEM damage model.

Kinematic Calibration of an Industrial Manipulator without External Measurement Devices

This paper presents a practical approach to fully automated kinematic calibration of an industrial manipulator. The approach is based on the principle of plane constraint. The electrical signal is used to fix the moment of contact between the conductive tool and the flat surface. The measurement data are manipulator configurations (joint angles) at the moment of contact. A modification of the algorithm to deal with the scaling problem is also proposed. This approach provides both high calibration accuracy and lower cost of the experimental setup compared to coordinate measuring machines (CMMs), laser trackers, and vision systems. The article examines the impact of various methods of kinematic parameterization of manipulators: the Denavit – Hartenberg agreement (DH), product of exponentials (POE), as well as the complete and parametrically continuous model (CPC) on the calibration accuracy. A comparison is made of the open-loop and the proposed closed-loop calibration methods on the Puma 560 model known in the literature. POE parameters were converted to DH and CPC to compare accuracy after calibration based on these parameterizations. The method of computing POE-CPC transformation as a solution to a certain optimization problem is proposed. The problem of identifying geometric parameters in the presence of restrictions is solved by gradient optimization methods. Experiments have been carried out on an ABB IRB 1600 industrial manipulator with an installed conductive probe and an ABB IRBP A-500 robotic positioner with a conductive metal flat surface. A technique for indirectly checking the accuracy of calibration of kinematic parameters is proposed based on a study of the accuracy of manipulation when using these parameters.

Plane Constraints Aided Multi-Vehicle Cooperative Positioning Using Factor Graph Optimization

Plane Constraints Aided Multi-Vehicle Cooperative Positioning Using Factor Graph Optimization

A robust direct linear transformation for camera pose estimation using points

Camera pose estimation from 3D points and their 2D projections, known as the perspective-n-point (PnP) problem, is a fundamental task in computer vision. In this paper, we propose a robust direct linear transformation method for the PnP problem. The novelty lies in that a plane constraint based on the pinhole camera model is introduced, and an over-constrained linear equation system is obtained by merging the linear form of both the measured point correspondences and the plane constraint. As a result, our method successfully reduces the minimal number of point correspondences required by the linear solver to 4, and yields a unique solution for 4-point, 5-point, and n-point pose estimation. Experiment results, using both synthetic and real data, show that our method is substantially faster, and offers the accuracy and precision comparable with that of state-of-the-art methods.

A stable vision-based pose measurement approach by fusing the plane constraints formed by the control points and optical center

Due to the existence of noise, the non-iterative pose measurement approaches tend to be unstable when there are no redundant 3D control points. The pose measurement is easily affected by the measurement factors, i.e., the shape composed of the control points, the size of the shape composed of the control points, and the distance between the camera and control points. In order to enhance the anti-noise ability, thereby reducing the influence of the measurement factors, reducing the pose measurement error, and improving the pose measurement stability, a non-iterative pose measurement approach is proposed based on fusing the plane constraints formed by the control points and optical center. In addition to the correspondence between each control point and the corresponding image point, the length between any two control points is considered. The length constraint is converted to the plane constraint formed by these two control points and the optical center because the plane constraint can be fused in the non-iterative pose measurement model established by the correspondence between each control point and the corresponding image point. Experimental results show the advantages of our pose measurement approach.

A road surface filtering method based on plane normal adaptive constraints

Aiming at the problem that the road surface point cloud is difficult to filter due to the complex road conditions and road types, this study proposes a road surface filtering method with plane normal adaptive constraints. After the grid of point cloud data, extracting the road surface seed points by the approximate plane constraint method and establishing the eight-neighborhood grid. We use the improved random sample consensus plane fitting to extract the geometric features of the road surface and carry out the plane normal statistical analysis of the road surface grid. The normal distribution Quantile-Quantile plot and Chi-square test show that the normal vector distribution of the road surface grid conforms to the normal distribution. Establishing the plane normal adaptive parameters using the normal distribution probability-weighted mean and the normal distribution standard deviation, and the plane feature part is automatically constrained. After pretreatment by elevation constraint and filter point re-determination, the road surface area is grown by cluster analysis to obtain the ground point accurately cloud. Through experiments and analysis covering most types of road sections, such as expressway and urban roads, straight and curved roads, gentle roads, and undulating roads, the results show that the road surface point filtering precision, recall rate, and overall accuracy of this method are all above 97 %, which verifies the accuracy and reliability of the method.

A study of interactive furnishing design in the context of digitalization with a view to functional requirements

Abstract Firstly, an optimization algorithm based on planar design constraints is proposed, which can automatically generate interior space furnishings. The main idea of the algorithm is to represent the room units in interior space furnishings with multiple rectangular combinations of polygons and to translate the constraints and rules in furnishing design into relevant rectangular constraints. In order to improve the applicability of the method, this study also proposes a hierarchical algorithm flow based on the above method. Then, the interactive toilet aid design and the new intelligent wheelchair design, and the intelligent and ecological interior furnishing design are used as examples to explore the practice of interactive furnishing design based on multi-functional needs, which meet the emotional, psychological, and physiological needs of users. Based on the data analysis of furnishing design by hierarchical algorithm, the results show that the average score of ordinary users in the comparison results with Humans is 13.37 the average score of designers is 11.72; the MIQP model can be used for interior space furnishing design of residential buildings and can be extended to apply to space furnishing of large-scale scenes, MIQP method is much higher than existing methods in terms of efficiency The MIQP method is much more efficient than existing methods. This study improves the quality and level of furnishing design and promotes innovation in the design industry.

Camera and LiDAR Fusion for Urban Scene Reconstruction and Novel View Synthesis via Voxel-Based Neural Radiance Fields

3D reconstruction of urban scenes is an important research topic in remote sensing. Neural Radiance Fields (NeRFs) offer an efficient solution for both structure recovery and novel view synthesis. The realistic 3D urban models generated by NeRFs have potential future applications in simulation for autonomous driving, as well as in Augmented and Virtual Reality (AR/VR) experiences. Previous NeRF methods struggle with large-scale, urban environments. Due to the limited model capability of NeRF, directly applying them to urban environments may result in noticeable artifacts in synthesized images and inferior visual fidelity. To address this challenge, we propose a sparse voxel-based NeRF. First, our approach leverages LiDAR odometry to refine frame-by-frame LiDAR point cloud alignment and derive accurate initial camera pose through joint LiDAR-camera calibration. Second, we partition the space into sparse voxels and perform voxel interpolation based on 3D LiDAR point clouds, and then construct a voxel octree structure to disregard empty voxels during subsequent ray sampling in the NeRF, which can increase the rendering speed. Finally, the depth information provided by the 3D point cloud on each viewpoint image supervises our NeRF model, which is further optimized using a depth consistency loss function and a plane constraint loss function. In the real-world urban scenes, our method significantly reduces the training time to around an hour and enhances reconstruction quality with a PSNR improvement of 1–2 dB, outperforming other state-of-the-art NeRF models.