Deviation Of Points Research Articles

The comparison of the process of manual and robotic positioning of the electrode performing radiofrequency ablation under the control of a surgical navigation system

This study is aimed at the comparison of the process of manual and robotic positioning of the electrode performing radiofrequency ablation under the control of a surgical navigation system. The main hypothesis of this experiment was that the use of a collaborative manipulator (KUKA iiwa) will allow to position the active part of the electrode relative to the center of the tumor more accurately and from the first attempt. We also monitor the stability of the electrode′s velocity during insertion and consider some advantages in ergonomics using the robotic manipulator. We use three more criteria to compare the surgeon's and robotic performance, unlike other studies, where only the target point's accuracy criterion is observed. The main idea is to examine the movement parameters of the electrode that can lead to potential patient trauma. Sphere-shaped tumor phantoms measuring 8 mm in diameter were filled with contrast and inserted in bovine livers. 10 livers were used for the robotic experiment and an equal quantity for manual surgery. The livers were encased in silicone phantoms designed to imitate the liver position in a real patient's abdominal cavity. Analysis of CT data gave the opportunity to find the entry and the target point for each tumor phantom. This data was loaded into a surgical navigation system that was used to track and record the position of the RF-electrode during the operation for further analysis. The standard deviation of points from the programmed linear trajectory totaled in the average 0.3 mm for the robotic experiment and 2.33 mm for the manual operation with a maximum deviation of 0.55 mm and 7.99 mm respectively. Standard deviation from the target point was 2.69 mm for the collaborative method and 2.49 mm for the manual method. The average velocity was 2.97 mm/s for the manipulator and 3.12 mm/s for the manual method, but the standard deviation of the velocity relative to the value of the average velocity was 0.66 mm/s and 3.05 mm/s respectively. Thus, in two criteria out of three, the manipulator is superior to the surgeon, and equality is established in one. Surgeons also noticed advantages in ergonomics performing the procedure using the manipulator. This experiment was produced as part of the work on the developing of a robotic multifunctional surgical complex. We can confirm the potential advantages of using collaborative robotic manipulators for minimally invasive surgery in case of practice for cancer treatment.

Evaluation of Thermographic Imaging in Canine Hindlimb Muscles After 6 Min of Walking-A Pilot Study.

Objective: To evaluate changes in superficial temperature of hindlimb muscles before and after a 6-min walk in healthy dogs.Methods: Two infrared thermographic images were captured of the proximal and distal hindlimbs of 11 healthy dogs before and after a 6-min walk. Orthopedic exam and objective gait analysis confirmed the healthy status of study subjects. Superficial temperatures of the gastrocnemius, biceps femoris, and gracilis were assessed. Analysis of images was performed using 2 different methods of region of interest (ROI) selection. ROI were selected first using one point (single pixel) in the muscle and then separately by selecting a line (LN) corresponding to many points of each muscle belly from which an average was taken. P < 0.05 was considered significant.Results: There was no significant change in temperature using point ROI before and after 6 min of walking of the gastrocnemius, gracilis, and biceps femoris muscles (p = 0.273, p = 0.349, p = 0.351, respectively). Using linear ROI, both biceps femoris and gracilis muscles exhibited significant increases in temperature (p < 0.0001, p = 0.032, respectively). There was no significant increase in temperature of gastrocnemius muscle for both point and linear ROI selection (p = 0.273, p = 0.448, respectively). The right biceps femoris temperatures were higher compared to left biceps femoris using the linear ROI before and after walks (p < 0.0001). The overall (left and right limbs pooled) standard deviation of point selected values were greater than LN selected values of the biceps femoris (1.35 and 1.11) and gastrocnemius (1.51 and 1.23). In contrast, standard deviation for the gracilis measurements were decreased using point selection vs. LN selection (1.09 and 1.3).Conclusions: The biceps femoris and gracilis muscles demonstrated significant increases in surface temperature after 6 min of walking using the linear method of ROI. Measurement of numerous points along the entire length of the biceps femoris and gastrocnemius muscles may provide a more accurate assessment of the increased vascularity within the tissues resulting from work compared to single point selection.Clinical Significance: Prior activity and ROI selection method should be considered when interpreting thermography results.

Effects of spatial decomposition on the efficiency of kNN search in spatial interpolations

ABSTRACT Spatial interpolations are commonly used in geometric modelling in life science applications such as medical image processing. In large-scale spatial interpolations, it is always needed to find a local set of data points for each interpolated point using the k Nearest Neighbor (kNN) search. To improve the efficiency of kNN, the uniform grid is commonly employed to fastly locate neighbours, and the size of grid cell could strongly affect the efficiency of kNN search. In this paper, we evaluate effects of the size of uniform grid cell on the efficiency of kNN search which is implemented on the CPU and GPU. We employ the Standard Deviation of points’ coordinates to measure the spatial distribution of scattered points. For irregularly distributed scattered points, we perform several series of kNN search in two- and three-dimensions. Benchmark results indicate that: for both the sequential version implemented on the CPU and the parallel version implemented on the GPU, with the increase of the Standard Deviation of points’ coordinates, the relatively optimal size of the grid cell decreases and eventually converges. Moreover, relationships between the Standard Deviation of scattered points’ coordinates and the relatively optimal size of grid cell are fitted.

Feasibility and limitations of quantitative SPECT for 223Ra

The aim of this paper is to investigate the feasibility and limitations of activity-concentration estimation for 223Ra using SPECT. Phantom measurements are performed using spheres (volumes 5.5 mL to 26.4 mL, concentrations 1.6 kBq mL−1 to 4.5 kBq mL−1). Furthermore, SPECT projections are simulated using the SIMIND Monte Carlo program for two geometries, one similar to the physical phantom and the other being an anthropomorphic phantom with added lesions (volumes 34 mL to 100 mL, concentrations 0.5 kBq mL−1 to 4 kBq mL−1). Medium-energy and high-energy collimators, 60 projections with 55 s per projection and a 20% energy window at 82 keV are employed. For the Monte Carlo simulated images, Poisson-distributed noise is added in ten noise realizations. Reconstruction is performed (OS-EM, 40 iterations, 6 subsets) employing compensation for attenuation, scatter, and collimator-detector response. The estimated concentrations in the anthropomorphic phantom are also corrected using recovery coefficients. Errors for the largest sphere in the physical phantom range from −25% to −34% for the medium-energy collimator and larger deviations for smaller spheres. Corresponding results for the high-energy collimator are −15% to −31%. The corresponding Monte Carlo simulations show standard deviations of a few percentage points. For the anthropomorphic phantom, before application of recovery coefficients the bias ranges from −16% to −46% (medium-energy collimator) and −10% to −28% (high-energy collimator), with standard deviations of 2% to 14% and 1% to 16%. After the application of recovery coefficients, the biases range from −3% to −35% (medium energy collimator) and from 0% to −18%. The errors decrease with increasing concentrations. Activity-concentration estimation of 223Ra with SPECT is feasible, but problems with repeatability need to be further studied.

Novel finite element-based plate design for bridging mandibular defects: Reducing mechanical failure.

IntroductionWhen the application of a free vascularised flap is not possible, a segmental mandibular defect is often reconstructed using a conventional reconstruction plate. Mechanical failure of such reconstructions is mostly caused by plate fracture and screw pull‐out. This study aims to develop a reliable, mechanically superior, yet slender patient‐specific reconstruction plate that reduces failure due to these causes.Patients and MethodsEight patients were included in the study. Indications were as follows: fractured reconstruction plate (2), loosened screws (1) and primary reconstruction of a mandibular continuity defect (5). Failed conventional reconstructions were studied using finite element analysis (FEA). A 3D virtual surgical plan (3D‐VSP) with a novel patient‐specific (PS) titanium plate was developed for each patient. Postoperative CBCT scanning was performed to validate reconstruction accuracy.ResultsAll PS plates were placed accurately according to the 3D‐VSP. Mean 3D screw entry point deviation was 1.54 mm (SD: 0.85, R: 0.10–3.19), and mean screw angular deviation was 5.76° (SD: 3.27, R: 1.26–16.62). FEA indicated decreased stress and screw pull‐out inducing forces. No mechanical failures appeared (mean follow‐up: 16 months, R: 7–29).ConclusionReconstructing mandibular continuity defects with bookshelf‐reconstruction plates with FEA underpinning the design seems to reduce the risk of screw pull‐out and plate fractures.

Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle

The series hybrid electric powertrain is the main architecture of the hybrid electric tracked vehicle. For a series tracked hybrid electric bulldozer (HEB), frequent fluctuations of the engine working points, deviation of the genset working points from the pre-set target trajectory due to an insufficient response, or interference of the hydraulic pump consumed torque, will all result in increased fuel consumption. To solve the three problems of fuel economy, an adaptive smooth power following (ASPF) control strategy based on an optimal efficiency map is proposed. The strategy combines a fuzzy adaptive filter algorithm with a genset’s optimal efficiency, which can adaptively smooth the working points of the genset and search the trajectory for the genset’s best efficiency when the hydraulic pump torque is involved. In this study, the proposed strategy was compared on the established HEB hardware in loop (HIL) platform with two other strategies: a power following strategy in a preliminarily practical application (PF1) and a typical power following strategy based on the engine minimum fuel consumption curve (PF2). The results of the comparison show that (1) the proposed approach can significantly reduce the fluctuation and pre-set trajectory deviation of the engine and generator working points; (2) the ASPF strategy achieves a 7.8% improvement in the equivalent fuel saving ratio (EFSR) over the PF1 strategy, and a 3.4% better ratio than the PF2 strategy; and (3) the ASPF strategy can be implemented online with a practical controller.

Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

ABSTRACTDespite the large number of studies conducted during the last three decades concerning 3D building modelling starting from Light detection and ranging (Lidar) data, two persistent problems still exist. The first one is the absence of some roof details, which will not only disappear in the building roof model due to their small areas regarding the point density but are also considered as undesirable noise among the modelling procedures. The second problem consists in that the involved segmentation algorithms do not perform well in the presence of noise in the building point cloud data. These two problems generate undesirable deformation in the final 3D building model. This paper proposes a new automatic approach for detecting and modelling the missing roof details in addition to improving the building roof segments. In this context, the error map matrix, which presents the deviations of points to their fitting planes, is considered. Moreover, this matrix is analysed in order to deduce the mask of missing roof details. At this stage, a new numeric factor is defined for estimating the roof segmentation accuracy in addition to the validity of the roof segmentation result. Then, the building point cloud is enhanced in order to decrease the negative noise influence and, consequently, to improve the building roof segments. Finally, the functionality and the accuracy of the proposed approach are tested and discussed.

Thermal Deformation Compensation of Laser Tracker Relocating in Aircraft Assembly

During aircraft assembly, measurements from either a single or multiple laser tracker with different positions are transformed to an assembly coordinate system by measuring the enhanced reference system (ERS) points via a rigid-body registration method. The location deviations of the ERS points resulting from thermal deformations have become the major error source in registration. To improve the accuracy of measurement, a thermal deformation compensation method, based on precise thermal deformation theory, is proposed in this study. Thermal deformation coefficients of ERS points in X-, Y-, and Z-axes are calculated separately, and an iterative algorithm is developed to correct the actual locations of the ERS points and estimate the optimal coordinate transformation parameters. A case study indicated that, with our proposed novel thermal deformation compensation method, the average registration error is decreased by 9.2–38.7%, and the maximum registration error is deceased by 12.3–38.6%.

Isobaric heat capacity and standard thermodynamic properties of cation-ordered layered perovskite-like oxides NaLnTiO4 and A2Ln2Ti3O10 (A = H, Na, K; Ln = La, Nd, Gd)

The heat capacities of protonated layered perovskite-like oxides H1.64K0.36Nd2Ti3O10·0.61 H2O and H1.89K0.11Nd2Ti3O10·0.60 H2O were measured by precision adiabatic vacuum calorimetry over the temperature range of (6–300) K. The standard thermodynamic functions: molar heat capacity, enthalpy, entropy, and the Gibbs energy of the compounds were evaluated from the experimental data of the heat capacities over the temperature range of T = (6–300) K. The deviation of experimental points from fitting curves obtained on the basis of Debye’s theory. The overview on low-temperature heat capacity and standard thermodynamic properties of cation-ordered layered perovskite-like titanates NaLnTiO4 and A2Ln2Ti3O10 (A=H, Na, K; Ln=La, Nd, Gd) is present.

Multifidelity Modeling by Polynomial Chaos-Based Cokriging to Enable Efficient Model-Based Reliability Analysis of NDT Systems

This work proposes a novel multifidelity metamodeling approach, the polynomial chaos-based Cokriging (PC-Cokriging). The proposed approach is used for fast uncertainty propagation in a reliability analysis of nondestructive testing systems using model-assisted probability of detection (MAPOD). In particular, PC-Cokriging is a multivariate version of polynomial chaos-based Kriging (PC-Kriging), which aims at combining the advantages of the regression-based polynomial chaos expansions and the interpolation-based Kriging metamodeling methods. Following a similar process as Cokriging, the PC-Cokriging advances PC-Kriging by enabling the incorporation of multifidelity physics information. The proposed PC-Cokriging is demonstrated on two analytical functions and three ultrasonic testing MAPOD cases. The results show that PC-Cokriging outperforms the state-of-the-art metamodeling approaches when providing the same number of training points. Specifically, PC-Cokriging reduces the high-fidelity training sample cost of the Kriging and PCE metamodels by over one order of magnitude, and the PC-Kriging and conventional Cokriging multifidelity metamodeling by up to $$50\%$$ to reach the same accuracy level (defined by the root mean squared error being no greater than $$1\%$$ of the standard deviation of the testing points). The accuracy and robustness of the proposed method of the key MAPOD metrics versus various detection thresholds are investigated and satisfactory results are obtained.

Demand-responsive transit systems in areas with low transport demand of “smart city”

The development of modern telecommunications allows the concept of a “smart city” to increase the flexibility of public transportation significantly and, thus, improve population mobility. In this regard, public transportation represents a demand-responsive transit (DRT) system. This system occupies an intermediate position between route systems and taxi services and ensures flexibility in providing on-demand transport services. The article describes the main technologies that can be used in the DRT system, including those with fixed routes (Flexible-Route Segments, Request Stops) and without fixed routes (Demand-Responsive Connector, Feeder Service, Zone Route, Point Deviation). Despite the wide international experience in implementing DRT in practice, there are no clear criteria to determine the conditions, under which it is reasonable to introduce a particular technology of flexible service for passengers. The article systematizes the factors that determine the feasibility of transferring a bus route to the DRT system in particular time intervals and also determines a sequence of actions for the introduction of flexible modes of public transport services. The considered technologies to manage public transportation on demand show that it is possible to combine regular traffic and demand-responsive service, using these modes at different times. Such an approach allows reducing the carrier’s costs while maintaining or even improving the quality of public transportation compared with systems implementing only flexible service modes.

Does the Psychopathology of the Parents Predict the Developmental-Emotional Problems of the Toddlers?

Parental psychopathology has been defined in respect of psychopathological development in early childhood. This study aimed to investigate the effects of parental psychopathologies on social and emotional problems in the age range of 1-3 years and to determine children at risk. The study data were obtained from the 2009 Early Childhood Mental Health Profile taking population distribution into consideration with the properties representing Turkey. The primary caregiver of the child completed the Psychiatric Evaluation Form for 1-3 years, the Brief Infant-Toddler Social Emotional Assessment (BITSEA), the Ages and Stages Questionnaire (ASQ), and the Brief Symptom Inventory (BSI) for themselves. Machine learning models used for prediction. The performance of prediction models was evaluated with the ten-fold cross-validation method. Area Under Curve (AUC) values were calculated with Receiver Operating Characteristic (ROC) curves to evaluate the performance of each model. The evaluation was made of the data of 2775 children, comprising 1507 (54.3%) males and 1268 (45.7%) females with a mean age of 26.19±9.11 months (range, 10-48 months). A total of 106 children were identified as at risk, as they were above the clinical cut-off point (1.5 standard deviations) of the BITSEA points and below the cut-off points of any one of the developmental areas of the ASQ. Modeling was applied to the data of these 106 children. The Support Vector Machines (SVM) model was selected for prediction with the automatically optimized highest AUC value. Weighting for the SVM algorithm showed mothers' BSI scores, fathers' education and health problems, duration of breastfeeding, unplanned pregnancy are significant for predicting BITSEA-problem scores in the model. To be able to understand the complex relationship with parental psychopathology and behavioral problems, machine learning methods were used successfully in this study. Further studies with more massive data sets, more extended follow-up periods, and stronger algorithms will be able to identify risk groups earlier and allow early interventions to be implemented.

An Efficient v-Minimum Absolute Deviation Distribution Regression Machine

Support Vector Regression (SVR) and its variants are widely used regression algorithms, and they have demonstrated high generalization ability. This research proposes a new SVR-based regressor: v-minimum absolute deviation distribution regression (v-MADR) machine. Instead of merely minimizing structural risk, as with v-SVR, v-MADR aims to achieve better generalization performance by minimizing both the absolute regression deviation mean and the absolute regression deviation variance, which takes into account the positive and negative values of the regression deviation of sample points. For optimization, we propose a dual coordinate descent (DCD) algorithm for small sample problems, and we also propose an averaged stochastic gradient descent (ASGD) algorithm for large-scale problems. Furthermore, we study the statistical property of v-MADR that leads to a bound on the expectation of error. The experimental results on both artificial and real datasets indicate that our v-MADR has significant improvement in generalization performance with less training time compared to the widely used v-SVR, LS-SVR, e-TSVR, and linear e-SVR. Finally, we open source the code of v-MADR at https://github.com/AsunaYY/v-MADR for wider dissemination.

ГЕОМЕТРИЧНЕ МОДЕЛЮВАННЯ ПРОФІЛЮ ЛОПАТКИ ОСЬОВОГО КОМПРЕСОРА S-ПОДІБНОЇ ФОРМИ

Пропонується метод геометричного моделювання профілю лопатки осьового компресора S-подібної форми, який передбачає розподіл добре відпрацьованого симетричного аеродинамічного профілю вздовж середньої лінії з її перегином у вихідній ділянці модельованого профілю. Середня лінія профілю подається складеною кривою та формується з двох ділянок. Обидві ділянки складеної кривої моделюються у натуральній параметризації, де параметром виступає довжина дуги лінії, а також застосуванні певних законів розподілу кривини. Кривина першої ділянки підпорядковується квадратичній залежності від довжини дуги, друга ділянка моделюється із застосуванням поліноміальної залежності п’ятого степеня. В точці стикування ділянок, розташованій в місці максимального підйому середньої лінії, забезпечується третій порядок гладкості, який передбачає в цій точці рівність значень функцій, її похідних, кривини та похідних від кривини по довжині дуги. Вихідними даними для моделювання середньої лінії виступають координати чотирьох точок, дві з яких знаходяться в початковій та кінцевій точках модельованої кривої, третя точка – в місці максимального підйому середньої лінії, четверта точка – в місці розташування перегину кривої. В початковій та кінцевій точках задаються кути нахилу дотичних, які визначаються в газодинамічному розрахунку осьового компресора та враховують кут установки профілю. В точці максимального підйому лінії кут нахилу дотичної дорівнює нулю. В точці перегину середньої лінії, що надає профілю S-подібність, кут нахилу дотичної не задається, оскільки він визначається в процесі моделювання кривої. Також у процесі моделювання ділянок кривої знаходяться невідомі коефіцієнти залежностей розподілу кривини та довжини дуг. Це реалізується шляхом мінімізації відхилення проміжно отриманих точок від заданих базових точок. На першій ділянці задача мінімізації є однокритеріальною, на другій – двокритеріальною, оскільки модельована лінія має пройти через задану проектантом точку перегину середньої лінії. Саме наявність перегину середньої лінії дозволяє отримувати профілі лопаток осьових компресорів S-подібної форми.

Large range closed-loop scans with a focal-distance-modulated confocal sensor

The article presents large range closed-loop scans with a high-speed focal-distance-modulated fibre-coupled confocal sensor integrated into a nano measuring machine (NMM-1). Combined with an appropriate signal processing this axial beam modulating sensor yields a linear characteristic curve, which enables areal closed-loop scans. Such scans are conducted under thermally stable conditions on a concave mirror with a nominal focal length of 19 mm. Prior to the scans the mirror is used to determine and adjust the probe direction of the sensor in order to align the probe direction with the z-axis of the NMM-1. Free-form scans without specification of the geometry of the concave mirror and scans with the specification of the spherical geometry are compared regarding the achievable scan velocity and the influence of the scan velocity on the achievable measurement precision of profile scans. The measurement precision for these scans over a double-digit millimeter-range is in the low double-digit nanometer-range. Finally, the ultimate areal measurement precision is calculated by the standard deviation of each single measurement point of repeated areal scans of the concave mirror.

Influence of body and head posture on the deviation of the condylar point during dental treatment

Influence of body and head posture on the deviation of the condylar point during dental treatment

Tree Skeletonization for Raw Point Cloud Exploiting Cylindrical Shape Prior

Tree skeleton extraction plays a fundamental role in reconstructing both biological and structural models of trees. However, traditional approaches can be ineffective and problematic in guaranteeing the topological correctness and centeredness of the tree skeleton when the tree point clouds contain noise and occlusions. To overcome this limitation, we present a tree skeletonization method to generate topologically correct and well-centered tree skeletons. We extract an initial skeleton from the tree point clouds via an octree and level set method, use cylindrical prior constraint (CPC) optimization and the estimated radii of branches to yield corrected positions of improper joints, and finally obtain updated skeletons with improved smoothness. The good centeredness of our proposed method is intrinsically achieved by (1) exploiting the cylindrical shape prior and calculating the CPC in the local neighborhood and (2) feeding the prior knowledge regarding the radii of tree branches into a topology refinement algorithm to yield near-optimal estimates of the positions of the skeleton points. To evaluate our method, we construct a novel tree point cloud data set with known ground truth and propose three quantitative assessment methods: skeleton points deviation (SPD), bifurcation points coverage (BPC) and endpoints coverage (EPC). The quantitative assessment and visual assessment show that the proposed method clearly outperforms traditional ones in terms of topology correctness and centeredness of the extracted tree skeleton.

Real-Time Prediction of Curing Processes using Model Order Reduction

Manifold engineering applications are directly affected by temperature. For rubber or composite curing processes, temperature distributions over time inside the compounds are crucial for chemical cross-linking reactions. Most of these reactions occur subsequently to a heating process during product cool down. Online prediction of cooling phases is performed during the actual heating process and hence, final cure status can be estimated before the actual process finishes. Therefore, mold temperatures and heating duration can be adapted in regard to current ambient conditions, and hence product quality is increased. In order to achieve longterm thermal predictions for complex product geometries, simulating nonlinear thermal finite element models is unfeasible, due to high computational effort. Hence, a prediction-model is derived from finite element analysis using matrix export, linearization, model order reduction algorithms such as rational Krylov or iterative rational Krylov and correction of operating point deviation. A special remark is given to temperature dependent boundary conditions, choice of time discretization and choice of solving algorithm, to address arising conflicting goals between execution time and simulation accuracy. Eventually, a complete process simulation is performed during the task-cycle time on a PLC control with a sufficiently high accuracy.

DeepWindow: Sliding Window Based on Deep Learning for Road Extraction From Remote Sensing Images

The road centerline extraction is the key step of the road network extraction and modeling. The hand-craft feature engineering in the traditional road extraction methods is unstable, which makes the extracted road centerline deviated from the road center in complex cases and even results in overall extracting errors. Recently, the road centerline extraction methods based on semantic segmentation employing deep neural network greatly outperformed the traditional methods. Nevertheless, the pixel-wise labels for training deep learning models are expensive and the postprocess of road segmentation is error-prone. Inspired by the work of human pose estimation, we propose DeepWindow, a novel method to automatically extract the road network from remote sensing images. DeepWindow uses a sliding window guided by a CNN-based decision function to track the road network directly from the images without the prior of road segmentation. First of all, we design and train a CNN model to estimate the road center points inside a patch. Then, the road seeds are automatically searched patch by patch employing the CNN model. Finally, starting from seeds, our method first estimates the road direction using a Fourier spectrum analysis algorithm and then iteratively tracks the road center-line along the road direction guided by the CNN model. In our method, the CNN model is trained by point annotations, which greatly reduces the training costs comparing to those in semantic model training. Our method achieves comparable performance with the state-of-the-art road extraction methods, and extensive experiments indicate that our method is robust to the point deviation.

Kinematic and Dynamic Vehicle Model-Assisted Global Positioning Method for Autonomous Vehicles with Low-Cost GPS/Camera/In-Vehicle Sensors.

Real-time, precise and low-cost vehicular positioning systems associated with global continuous coordinates are needed for path planning and motion control in autonomous vehicles. However, existing positioning systems do not perform well in urban canyons, tunnels and indoor parking lots. To address this issue, this paper proposes a multi-sensor positioning system that combines a global positioning system (GPS), a camera and in-vehicle sensors assisted by kinematic and dynamic vehicle models. First, the system eliminates image blurring and removes false feature correspondences to ensure the local accuracy and stability of the visual simultaneous localisation and mapping (SLAM) algorithm. Next, the global GPS coordinates are transferred to a local coordinate system that is consistent with the visual SLAM process, and the GPS and visual SLAM tracks are calibrated with the improved weighted iterative closest point and least absolute deviation methods. Finally, an inverse coordinate system conversion is conducted to obtain the position in the global coordinate system. To improve the positioning accuracy, information from the in-vehicle sensors is fused with the interacting multiple-model extended Kalman filter based on kinematic and dynamic vehicle models. The developed algorithm was verified via intensive simulations and evaluated through experiments using KITTI benchmarks (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and data captured using our autonomous vehicle platform. The results show that the proposed positioning system improves the accuracy and reliability of positioning in environments in which the Global Navigation Satellite System is not available. The developed system is suitable for the positioning and navigation of autonomous vehicles.