Fine Alignment Research Articles

Hands-on robot-assisted fracture reduction system guided by a linear guidance constraints controller using a pre-operatively planned goal pose.

In robot-assisted fracture reduction systems, the fine alignment of the fractured bone and its path planning are still of issues that need to be resolved. A novel linear guidance constraints (LGC) controller guides a robot along the shortest path to align a distal fracture segment and a proximal one. In addition, the surgeon can modify the path whenever s/he wants. When the LGC controller is used in the experiment on a femoral bone model with simulated muscles, the fracture reduction time was measured to be 35.6±16.4seconds, while the position and the angle errors were 0.41±0.61mm, and 0.22±0.80°, respectively. The proposed controller reduced the reduction time by 78.1%, the translational error by 91.6%, and the angular error by 95.4%, compared with the cases without the LGC controller. It was proven that the proposed scheme reduced the reduction time and the pose error of the fracture alignment, and that it is effective to alleviate the maneuvering load.

An Improved Strapdown Inertial Navigation System Initial Alignment Algorithm for Unmanned Vehicles.

Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm.

ENHANCEMENT OF REAL-TIME SCAN MATCHING FOR UAV INDOOR NAVIGATION USING VEHICLE MODEL

Abstract. Autonomous Unmanned Aerial Vehicles (UAVs) have drawn great attention from different organizations, because of the various applications that save time, cost, effort, and human lives. The navigation of autonomous UAV mainly depends on the fusion between Global Navigation Satellite System (GNSS) and Inertial Measurement System (IMU). Navigation in indoor environments is a challenging task, because of the GNSS signal unavailability, especially when the utilized IMU is low-cost. Light Detection and Ranging Radar (LIDAR) is one of the mainly utilized sensors in the indoor environment for localization through scan matching of successive scans. The process of calculating the rotation and translation from successive scans can employ different approaches, such as Iterative Closest Point (ICP) with its variants, and Hector SLAM. ICP and Hector SLAM iterative fashion can greatly increase the matching time, and the convergence is not guaranteed in case of harsh maneuvers, moving objects, and short-range LIDAR as it may get stuck in local minima. This paper proposes enhanced real-time ICP and Hector SLAM algorithms based on vehicle model (VM) during sharp maneuvers. The vehicle model serves as initialization step (coarse alignment) then the ICP/Hector serve as fine alignment step. Test cases of quadcopter flight with harsh maneuvers were carried out with LIDAR to evaluate the proposed approach to enhance the ICP/Hector convergence time and accuracy. The proposed algorithm is convenient for UAVs where there are limitations regarding the size, weight, and power limitations, as it is a stand-alone algorithm that does not require any additional sensors.

Deduction of Surfactants Effect on Performance, Morphology, Thermal and Molecular Properties of Polymeric Polyvinylidene Fluoride (PVDF) Based Ultrafiltration Membrane

In membrane making, surfactant material is one of the effective strategies to produce membrane / thin film with optimal and specific properties. This paper addressed the effect of Sodium dodecyl sulphate (SDS), Triton X-100 and Tween 80 as additives for the making of Polyvinylidene fluoride (PVDF) ultrafiltration (UF) membranes. The fabricated membranes were analysed in the aspect of performance, morphological structures, thermal properties and molecular orientation. Analysis results showed that the surfactant additives enhanced the membrane performance and properties. Surprisingly, the addition of 2 wt% surfactant increased the permeate flux of bovine serum albumin (BSA) up to 78.14 L/m2h compared to membrane without surfactant with flux at only 2.74 L/m2h. In addition, the role of surfactants on membrane pores and pore properties of PVDF UF membranes were examined. The cross-sectional morphological structures showed that these surfactants led towards the formation of macro-voids and finger-like structures as well as a spongy layer. The surfactants additives also improved the membrane pores and pore properties which reflect to the protein rejection up to 85 % and 78 % of BSA and egg albumin (EA), respectively. Moreover, analysis on the thermal features found that SDS, Triton X-100 and Tween 80 promoted better thermal stability and significantly fine molecular alignment which provided the versatility for the membrane to be used for any applications.

In-Motion Filter-QUEST Alignment for Strapdown Inertial Navigation Systems

By analyzing the error models of the measured vectors of the gravitational apparent motion, an in-motion filter-QUEST alignment method only with the inertial measurement unit is presented in this paper. The contribution of the proposed method lies in constructing the in-motion model of the measured vectors of the gravitational apparent motion and developing the extracted method to reconstruct the measured vectors. Furthermore, the relationship between the noise characteristic and the moving state of the vehicle is analyzed in detail. Different from the several current techniques, the presented method can be carried out without any other external additional equipment, when the vehicle is in-motion. With the designed real-time wavelet denoising (RWD) method, the high-frequency noises of the measured vectors are filtered. Based on the constructed parameter recognition model of the measured vectors, a robust adaptive Kalman filter is devised to estimate the optimal parameters, which are used to calculate the reconstructed observation vectors. Moreover, the gross outliers, which are contained in the filtered vectors of the RWD, are eliminated effectively. The simulation and the field trial results demonstrate that the presented method is applicable to the in-motion initial alignment, and it can serve as a nice initial alignment method in the follow-on fine alignment process and navigation process.

Analytic coarse alignment and calibration for inertial navigation system on swaying base assisted by star sensor

Initial alignment and positioning assisted by star sensors have been a popular research direction for the inertial navigation system. In this study, an analytic method is proposed to achieve the integration implementation of coarse alignment, calibration and positioning for swaying base. Gyroscope bias, accelerometer bias and attitude estimations are given in analytical forms on the basis of the attitude matrix provided by the star sensor. Positioning can be realised by transforming the accelerometer calibration part when the position information is not available. Compared with the previous approaches, the proposed method establishes the intuitive relationship between the navigation parameters and sensor errors, and is easy to implement. Furthermore, the proposed method can be extended to moving base when the velocity measurement is available. Both the simulation and experimental results show that the proposal analytic method can provide both good initial attitude and sensor parameters for fine alignment process.

In-Motion Iterative Fine Alignment Algorithm for On-Board Vehicular Odometer-Aided SINS

This research proposes a novel in-motion fine alignment algorithm for vehicular dead reckoning (DR) with odometer-aided strapdown inertial navigation system (SINS) while the map matching result is used for a group of landmark points to estimate misalignment angles. The proposed algorithm is designed based on principle of similarity, that is, trajectory of DR is similar to the true trajectory that the main difference between these two trajectories is rotation and scale. Further, the results from map matching are introduced as a group of landmark points to estimate the residual of azimuth error angle after coarse alignment and the scale factor error of the odometer. It is theoretically proved that the alignment effectiveness based on the results from map matching is equivalent to that on single zero error landmark point. Finally, digital simulations are conducted to verify the presented algorithm and test the performance.

Autonomous In-motion Alignment for Land Vehicle Strapdown Inertial Navigation System without the Aid of External Sensors

This paper describes a fully autonomous real-time in-motion alignment algorithm for Strapdown Inertial Navigation Systems (SINS) in land vehicle applications. Once the initial position is available, the vehicle can start a mission immediately with accurate attitude, position and velocity information determined within ten minutes. This is achieved by two tightly coupled stages, that is, real-time Double-vector Attitude Determination Coarse Alignment (DADCA) and Backtracking Fine Alignment (BFA). In the DADCA process, the vehicle motion is omitted to roughly estimate the attitude at the very start of the alignment. Meanwhile, attitude quaternions and velocity increments are extracted and recorded. The BFA process utilises the stored data and exploits the Non-Holonomic Constraints (NHC) of a vehicle to obtain virtual velocity measurements. A linear SINS/NHC Kalman filter with mounting angles as extended states is constructed to improve the fine alignment accuracy. The method is verified by three vehicle tests, which shows that the accuracy of alignment azimuth is 0·0358° (Root Mean Square, RMS) and the positioning accuracy is about 15 m (RMS) at the end of the alignment.

Collective matrix of spatial light modulators for increased resolution in holographic image projection.

We present a method to increase the resolution of holographically projected images by the use of a collective matrix of two phase-only spatial light modulators. As a result of fine alignment and common coherent illumination of the modulators, a synthetic aperture is formed with a doubled number of active pixels. In this paper we present theoretical analysis and numerical simulations which stand in good agreement for different distances between the apertures of modulators. We have achieved experimentally controlled field interference from both modulators on the projection screen, exhibiting increased resolution in one spatial direction and residual effects of the dead space between modulators. Then we have proved numerically possibility to extend our approach on bigger synthetic apertures consisted of more than two modulators.

A Simple Robotic Eye-In-Hand Camera Positioning and Alignment Control Method Based on Parallelogram Features

A simple and effective method for camera positioning and alignment control for robotic pick-and-place tasks is described here. A parallelogram feature is encoded into each 3D object or target location. To determine the pose of each part and guide the robot precisely, a camera is mounted on the robot end-flange to determine and measure the location and pose of the part. The robot then adjusts the camera to align it with the located part so that it can be grasped. The overall robot control system follows a continuous look-and-move control strategy. After a position-based coarse alignment, a sequence of image-based fine alignment steps is carried out, and the part is then picked and placed by the robot arm gripper. Experimental results showed an excellent applicability of the proposed approach for pick-and-place tasks, and the overall errors were 1.2 mm for positioning and 1.0° for orientation angle.

Anti-Disturbance Initial Alignment Method Based on Quadratic Integral for Airborne Distributed POS

Airborne distributed position and orientation system (POS) is playing an important role in the high-resolution aerial survey and a remote sensing system. It is composed of a master inertial navigation system (INS), the global navigation satellite system, and several slave INSs. However, the high-frequency vibration disturbance of aircraft engine and the flexure deformation disturbance of lever arm could lead to serious initial alignment errors of slave INSs. In order to solve the problem, an anti-disturbance initial alignment method based on quadratic integral for airborne distributed POS is proposed. A coarse alignment based on quadratic integral in inertial frame is proposed to reduce the disturbances of high-frequency vibration and flexure deformation. Furthermore, the disturbance of flexure deformation is considered as the first-order Markov process, and is added into the fine transfer alignment model. The experimental results verified that the proposed method can effectively improve the initial alignment of the distributed POS, especially the azimuth precision of two slave INSs which are improved by 0.0260° and 0.0167°, respectively.

Global rigid registration of CT to video in laparoscopic liver surgery

PurposeImage-guidance systems have the potential to aid in laparoscopic interventions by providing sub-surface structure information and tumour localisation. The registration of a preoperative 3D image with the intraoperative laparoscopic video feed is an important component of image guidance, which should be fast, robust and cause minimal disruption to the surgical procedure. Most methods for rigid and non-rigid registration require a good initial alignment. However, in most research systems for abdominal surgery, the user has to manually rotate and translate the models, which is usually difficult to perform quickly and intuitively.MethodsWe propose a fast, global method for the initial rigid alignment between a 3D mesh derived from a preoperative CT of the liver and a surface reconstruction of the intraoperative scene. We formulate the shape matching problem as a quadratic assignment problem which minimises the dissimilarity between feature descriptors while enforcing geometrical consistency between all the feature points. We incorporate a novel constraint based on the liver contours which deals specifically with the challenges introduced by laparoscopic data.ResultsWe validate our proposed method on synthetic data, on a liver phantom and on retrospective clinical data acquired during a laparoscopic liver resection. We show robustness over reduced partial size and increasing levels of deformation. Our results on the phantom and on the real data show good initial alignment, which can successfully converge to the correct position using fine alignment techniques. Furthermore, since we can pre-process the CT scan before surgery, the proposed method runs faster than current algorithms.ConclusionThe proposed shape matching method can provide a fast, global initial registration, which can be further refined by fine alignment methods. This approach will lead to a more usable and intuitive image-guidance system for laparoscopic liver surgery.

Initial fine alignment based on self‐contained measurement in erection manoeuvre

The initial alignment is vital to the inertial navigation system of a cruise vehicle. The existing initial fine alignment methods in the erection process are mostly dependent on other equipment to provide measurements, which increase the complexity and cost of the system and may cause some measurement error and noise. To solve this problem, an initial fine alignment method based on self-contained measurement is proposed and executed during the erection process with a static base. The self-contained measurement is obtained through the modelling of a multi-level dynamic lever arm. Using the self-contained measurement, the fine alignment model is derived and the precision of initial fine alignment can be improved. Finally, experiments were carried out and verified the validity of the proposed method.

A Fast In-Field Coarse Alignment and Bias Estimation Method for Stationary Intermediate-Grade IMUs

This paper presents an innovative in-field coarse bias estimation (CBE) method for stationary intermediate-grade inertial measurement units (IMUs), which is based on the existence of normality and orthogonality errors in the attitude matrix supplied by the traditional three-axis attitude determination stationary coarse alignment (CA) stage. Differently from the currently existing in-field bias estimation formulations, the proposed CBE method is conceived to be conducted in stationary conditions, jointly with the CA, and it allows the down accelerometer bias and the north and down angular rate sensor biases to be adequately estimated. As a main contribution of this paper, we demonstrate that the proposed CBE method is able to greatly accelerate the estimation process of the aforementioned biases, contributing, hence, to shorten the IMU setup time. A comprehensive error analysis is presented, and the conclusions are twofold: 1) the proposed CBE method is valid regardless of the IMU orientation and 2) its estimation accuracy is mainly constrained by the north accelerometer bias and the position location uncertainty, which are, in general, very reduced. Results from simulated and experimental test confirm the adequacy of the outlined verifications, also attesting the superiority of the proposed CBE method in situations where, due to time constraints, stationary fine alignment procedures are not allowed to be carried out.

Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device

In vitro three-dimensional (3D) culturing is considered essential in many biological fields. However, the imaging of developed 3D formations is often difficult, especially if the size of the sample is relatively large. The z-resolution of fluorescent imaging is low using low magnification lenses (4× and 10×) due to large focal depths. This paper describes 3D culture platform enabling large scale 3D imaging by fine spatial alignment of the image dataset obtained from multiple directions. A gel cube device was employed to conduct the multi-scanning and then a self-fluorescent microstructure in a cubic frame allows us spatially align image dataset within a few pixels. By synthesizing data from multiple scans, the platform enables us to visualize millimeter-sized 3D sample structure and individual cellular actin filaments at the same time. Millimeter depth imaging of a developed bronchial tree was achieved with high z-resolution. The device, which is applicable to most microscopy systems, can enhance the image quality without modifying current systems.

Analytic Evaluation of Fine Alignment for Velocity Aided INS

Coarse attitude initialization procedure is used to determine the initial attitude of the inertial navigation system. To improve the accuracy of the attitude to a desired value, fine alignment (FA) process is employed. In stationary conditions, zero velocity updates are usually used to achieve such goal. In this paper, an analytic evaluation of the steady-state properties of the FA process is presented. The contribution of this paper is twofold: 1) closed-form analytic solutions to the error-state covariance of the FA process are derived and 2) semianalytic solutions to the convergence time of the error-state covariance are shown. With such closed-form solutions at hand, insight can be gained into the parameters involved in the FA process in preliminary design process. Simulation and field experiments are given to support the analytical expressions.

Hierarchical semantic image matching using CNN feature pyramid

Image matching remains an important and challenging problem in computer vision, especially for the dense correspondence estimation between images with high category-level similarity. The effectiveness of image matching largely depends on the advance of image descriptors. Inspired by the success of Convolutional Neural Network(CNN), we propose a hierarchal image matching method using the CNN feature pyramid, named as CNN Flow. The feature maps output by different layers of CNN tend to encode different information of the input image, such as the semantic information extracted from higher layers and the structural information extracted from lower layers. This nature of CNN feature pyramid is suitable to build the hierarchical image matching framework, which detects the patterns of different levels in an implicit coarse-to-fine manner. In particular, we take advantage of the complementarity of different layers using guidance from higher layer to lower layer. The high-layer features present semantic patterns to cope with the intra-class variations, and the guidance from high layers can resist the semantic ambiguity of low-layer features due to small receptive fields. The bottom-level matching utilize the low-layer features with more structural information to achieve finer matching. On one hand, extensive experiments and analysis demonstrate the superiority of CNN Flow in image dense matching under challenging variations. On the other hand, CNN Flow is demonstrated through various applications, such as fine alignment for intra-class object, scene label transfer and facial expression transfer.

Backtracking Velocity Denoising Based Autonomous In-Motion Initial Alignment

In the global navigation satellite system denial environment, strap-down inertial navigation system (SINS) has to rely on the body-frame velocity output by autonomous velocity measurement equipment, such as odometer and Doppler velocity log, to implement the in-motion initial alignment. Considering the external velocity noise and the approximation existing in the body-frame velocity aided initial alignment model, the autonomous in-motion alignment for SINS with high precision is a difficult problem. Besides, the position updating of SINS cannot be realized only depending on the body-frame velocity. The position error will negatively impact the precision of the following fine alignment and navigation. In this paper, a backtracking velocity denoising-based autonomous in-motion initial alignment is proposed. Forward compass alignment and backward compass alignment are carried out, respectively, to denoise the external velocity. Body-frame velocity-based and navigation-frame velocity-based attitude determination are implemented, respectively, to gradually realize the attitude alignment. The contributions of the work presented here are twofold. First, vehicle velocity during the whole initial alignment process is accurately denoised and determined based on the backtracking compass alignment. Second, high precision position alignment is achieved during the attitude determination. The validity of the proposed method is verified based on field test data.

External Velocity Aided Coarse Attitude and Position Alignment for Dynamic SINS

This paper concerns about the dynamic alignment for the strapdown inertial navigation system (SINS), especially in the case of no GPS assistance. A novel coarse alignment algorithm using external reference velocity in b-frame is proposed. To get the observation vectors of the optimization-based attitude alignment method, the velocity algorithm of SINS is derived in the inertial coordinate frame, so the Coriolis Effect can be excluded. Meanwhile, the real-time position of the SINS is approximately located, and the deviation of the gravity vector caused by the vehicle’s displacement can be compensated, which further improves the alignment accuracy. Experimental results show that the proposed algorithm performs better than the existing algorithm and it can provide ideal initial conditions for the fine alignment.

Fast and high-resolution light field acquisition using defocus modulation.

In the conventional microlens-array-based light field imaging system, there is a trade-off between the angular and spatial resolutions. Light field reconstruction from images captured by focal plane sweeping, such as light field moment imaging (LFMI) and light field reconstruction with back projection (LFBP), can achieve high transverse resolution comparable to the modern camera sensor. However, the acquisition of a series of focal plane sweeping images along the optical axis is time consuming and requires fine alignment. Furthermore, different focal-plane-based light field reconstruction techniques require images with different characteristics. To solve these problems, we present an efficient approach for fast light field acquisition with precise focal plane sweeping capture by defocus modulation, rather than mechanical movement. Also, we verify the validity and the improvement of our system. With the controllable point spread function, we can capture images for light field reconstruction with both LFMI and LFBP. Otherwise, we quantitatively compare the two methods using images captured with the proposed systems.