Grade Inertial Measurement Unit Research Articles

Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.

IMU-Assisted Manual 3D-Ultrasound Imaging Using Motion-Constrained Swept-Fan Scans.

Three-dimensional (3D) ultrasonic imaging can enable post-facto plane of interest selection. It can be performed with devices such as wobbler probes, matrix probes, and sensor-based probes. Ultrasound systems that support 3D-imaging are expensive with added hardware complexity compared to 2D-imaging systems. An inertial measurement unit (IMU) can potentially be used for 3D-imaging by using it to track the motion of a one-dimensional array probe and constraining its motion in one degree of freedom (1-DoF) rotation (swept-fan). This work demonstrates the feasibility of an affordable IMU-assisted manual 3D-ultrasound scanner (IAM3US). A consumer-grade IMU-assisted 3D scanner prototype is designed with two support structures for swept-fan. After proper IMU calibration, an appropriate KF-based algorithm estimates the probe orientation during the swept-fan. An improved scanline-based reconstruction method is used for volume reconstruction. The evaluation of the IAM3US system is done by imaging a tennis ball filled with water and the head region of a fetal phantom. From fetal phantom reconstructed volumes, suitable 2D planes are extracted for biparietal diameter (BPD) manual measurements. Later, in-vivo data is collected. The novel contributions of this paper are (1) the application of a recently proposed algorithm for orientation estimation of swept-fan for 3D imaging, chosen based on the noise characteristics of selected consumer grade IMU (2) assessment of the quality of the 1-DoF swept-fan scan with a deflection detector along with monitoring of maximum angular rate during the scan and (3) two probe holder designs to aid the operator in performing the 1-DoF rotational motion and (4) end-to-end 3D-imaging system-integration. Phantom studies and preliminary in-vivo obstetric scans performed on two patients illustrate the usability of the system for diagnosis purposes.

Low-Cost Real-Time PPP GNSS Aided INS for CAV Applications

Many connected and autonomous vehicle (CAV) applications benefit from navigation technologies that reliably achieve lane-level accuracy. Global organizations have recently begun to provide real-time common-mode error corrections for Global Navigation Satellite Systems (GNSS) that enable this level of positioning accuracy using Precise Point Positioning (PPP). Incorporating an Inertial Measurement Unit (IMU) with a GNSS receiver can achieve this positioning performance reliably on moving platforms while providing a full state estimate, with high bandwidth at a high sampling rate. For commercial automotive applications cost is critical; therefore, this article focuses on commercial grade IMU’s and single-frequency GNSS code and Doppler measurements. This article presents and demonstrates a tightly-coupled PPP GNSS aided Inertial Navigation System (INS) using only publicly available real-time data sources. The article discusses the role of the INS in automotive applications and how the quality of the IMU affects the ability to achieve that role. The experimental results demonstrate positioning accuracy that surpasses the Society of Automotive Engineering (SAE) J2945 specification (horizontal error ≤1.5 m and vertical error ≤ 3 m 68%).

Onboard Train Localization Based on Railway Track Irregularity Matching

Train localization is a task of fundamental importance to provide reliable and accurate train positioning information for railway control and operation. Determining the train’s position using onboard sensors is a technical trend in this field. In this research, we proposed a train positioning approach by matching a new kind of rail feature, that is, track irregularity. By using an onboard inertial navigation system (INS), continuous and location-dependent track irregularity can be sensed, and then, this signal measurement is matched up to the predefined track irregularity map to locate the train. Train-borne experiments were carried out to validate the feasibility and evaluate the navigation performance of the proposed method. The results show that 0.6-m positioning accuracy with 97.26% confidence can be achieved when using a typical tactical grade inertial measurement unit (IMU), and the accuracy is 0.8 m with 95.39% confidence for a low-cost microelectromechanical system (MEMS) IMU. The proposed method is proven to be able to enhance the robustness and accuracy of the multisensory train positioning system.

A novel matrix block algorithm based on cubature transformation fusing variational Bayesian scheme for position estimation applied to MEMS navigation system

It is great significant for autonomous underwater vehicle (AUV) to obtain its position in real time. Great developments in low-cost inertial navigation system (INS) have been made due to outstanding merits in micro-electro-mechanical system (MEMS) technologies. The navigation errors of the MEMS grade inertial measurement unit (IMU) increase greatly over time because of the complex and changeable marine environment. The measurement noise plays an important role in state estimation with high accuracy. However, the accuracy of measurement noise will be degraded due to larger MEMS sensors’ errors. To solve the problem above, a novel algorithm which fuses variational Bayesians into nonlinear filtering is proposed. Firstly, the position information is augmented to the measurement vector. The measurement functions are divided into linear and nonlinear. Secondly, the variational Bayesian (VB) method is used to estimate the probability density function of the state vector, predicted error covariance matrix and measurement noise matrix. In the case of calculating the second order moment estimation, the cubature transformation is used to determine nonlinear integral equations, and the linear integral equations are derived by the Kalman filter. Finally, the accurate state vector and error covariance matrix are obtained. The real underwater experiments are performed and experiment results show that the proposed algorithm has better performance in aspect of positioning accuracy of AUV and robustness than the traditional algorithms.

A Robust Adaptive Cubature Kalman Filter Based on SVD for Dual-Antenna GNSS/MIMU Tightly Coupled Integration

In complex urban environments, a single Global Navigation Satellite System (GNSS) is often not ideal for navigation due to a lack of sufficient visible satellites. Additionally, the heading angle error of a GNSS/micro-electro-mechanical system–grade inertial measurement unit (MIMU) tightly coupled integration based on the single antenna is large, and the attitude angle, velocity, and position calculated therein all have large errors. Considering the above problems, this paper designs a tightly coupled integration of GNSS/MIMU based on two GNSS antennas and proposes a singular value decomposition (SVD)-based robust adaptive cubature Kalman filter (SVD-RACKF) according to the model characteristics of the integration. In this integration, the high-accuracy heading angle of the carrier is obtained through two antennas, and the existing attitude angle information is used as the observation to constrain the filtering estimation. The proposed SVD-RACKF uses SVD to stabilize the numerical accuracy of the recursive filtering. Furthermore, the three-stage equivalent weight function and the adaptive adjustment factor are constructed to suppress the influence of the gross error and the abnormal state on the parameter estimation, respectively. A set of real measured data was employed for testing and analysis. The results show that dual antennas and dual systems can improve the positioning performance of the integrated system to a certain extent, and the proposed SVD-RACKF can accurately detect the gross errors of the observations and effectively suppress them. Compared with the cubature Kalman filter, the proposed filtering algorithm is more robust, with higher accuracy and reliability of parameter estimation.

A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms.

This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling.

Performance Evaluation of Different Grade IMUs for Diagnosis Applications in Land Vehicular Multi-Sensor Architectures

Vehicular positioning systems are necessary for the development of autonomous vehicles and advanced driver assistance systems (ADAS). In recent years, Inertial Measurement Units (IMU) based on micro-electromechanical systems (MEMS) have been included in proposals for multi-sensor positioning system architectures in order to take advantage of their cost and size. The measurement errors propagation to the positioning solution have limited its application for long-term positioning solution. However, it can play a key role in those applications where tri-axial attitude and accelerations can be indicators of curves, slopes, cants, etc. and it can be used as diagnostic of other sensors measurements. This work is focused on the use of IMUs for diagnostic applications and it compares medium-end (xSens MTi-100) and low-end (Bosch BMI160) grade MEMS-based IMUs in an experimental road test using a fusion of a high-end IMU (KVH GEO-FOG), GNSS and wheel speed sensor as reference. In addition, a research question about whether the calibration is the main reason between different grade IMUs has been formulated. Thus, a simple, manufacturable and cost-efficient calibration technique is applied to the low-end IMU in order to compare its performance improvement with the medium-end one. The raw measurements (angular rates and specific forces) and navigation states (tri-axial attitude and accelerations) are considered for diagnosis and they are statistically compared to evaluate the performance of each IMU. It is concluded that the calibration technique used makes the low-end IMU performance similar to that of the medium-end one. Consequently, this work contributes to optimizing the cost of land vehicular positioning systems when choosing the most appropriate sensor based on the accuracy and precision required for the application.

Impact of New GPS Signals on Positioning Accuracy for Urban Bus Operations

This paper analyses for the first time the impact of new GPS signals on positioning accuracy for dynamic urban applications, taking bus operations as an example. The performance assessment addresses both code measurement precision and positioning accuracy. The former is based on signal-to-noise ratio and estimation of multipath and noise by a combination of code and carrier phase measurements. The impact on positioning accuracy is derived by comparing the performance achievable with the conventional single frequency GPS only positioning both relative to reference trajectories from the integration of carrier phase measurements with data from a high grade inertial measurement unit. The results show that L5 code measurements have the highest precision, followed by L1 C/A and L2C. In the positioning domain, there is a significant improvement in two-dimensional and three-dimensional accuracy from dual frequency code measurements over the single frequency measurements, of 39% and 48% respectively, enabling more bus operation services to be supported.

Estimate the Pitch and Heading Mounting Angles of the IMU for Land Vehicular GNSS/INS Integrated System

Nonholonomic constraint (NHC) and odometer speed have been proven to significantly improve the navigation accuracy of a global navigation satellite system (GNSS)-aided inertial navigation system (INS) for land vehicular applications. Exploiting the full potential of the NHC and odometer aids requires the inertial measurement unit (IMU) mounting angles, i.e., angular misalignment with respect to the host vehicle, to be precisely known. We address the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach. In this method, DR using the GNSS/INS integrated attitude and distance traveled is fused with the GNSS/INS integrated position through a straightforward Kalman filter. Simulation and field tests are carried out to validate the proposed algorithm for different grade IMUs, including typical navigation-grade, tactical-grade and low-cost IMUs. The results demonstrate that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution, for example, 0.001° accuracy can be achieved for a navigation-grade GNSS/INS integrated system. The roll mounting angle can not be estimated due to lack of observability in this approach, and the heading mounting angle estimation may be influenced by the GNSS/INS heading accuracy drift to some extent for the low-cost IMUs.

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation.

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

Consistent ST-EKF for Long Distance Land Vehicle Navigation Based on SINS/OD Integration

This paper presents detailed derivations and further explanations of the state transformation extended Kalman filter (ST-EKF) from the common frame error definition perspective. Both the system error and measurement models of strapdown inertial navigation system (SINS)/Odometer (OD) tightly-coupled navigation are derived based on the ST-EKF. Both theoretical analysis and test results indicate the effectiveness of the proposed velocity error definition on mitigating the covariance-inconsistency problem that is caused by the specific force calculation error in the EKF state transition matrix. The excellent covariance-consistency of the ST-EKF makes it not necessary to remove gyro and accelerometer bias errors from the initial alignment Kalman filter states. This phenomenon can ensure the subsequent integrated navigation performance. Single-position ground SINS alignment experiments by using a navigation grade inertial measurement unit (IMU) showed that the estimated yaw angle from the conventional 15-state EKF slowly diverged over time. Furthermore, this phenomenon became more distinct when the initial yaw angle error was larger. In contrast, the estimated yaw angle from the proposed 15-state ST-EKF was significantly more stable and less degraded by large initial yaw angle errors. Long- distance land-vehicle SINS/OD integrated navigation tests also exhibited the ST-EKF's higher positioning and heading accuracy than the EKF.

Particle Imaging Velocimetry Gyroscope.

Inertial measurement units (IMUs) are typically classified as per the performance of the gyroscopes within each system. Consequently, it is critical for a system to have a low bias instability to have better performance. Nonetheless, there is no IMU available commercially that does not actually suffer from bias-instability, even for the navigation grade IMUs. This paper introduces the proposition of a novel fluid-based gyroscope, which is referred to hereafter as a particle imaging velocimetry gyroscope (PIVG). The main advantages of the PIVG include being nearly drift-free, a high signal-to-noise ratio (SNR) in comparison to commercially available high-end gyroscopes, and its low cost.

Optimized Multi-Position Calibration Method with Nonlinear Scale Factor for Inertial Measurement Units.

Navigation grade inertial measurement units (IMUs) should be calibrated after Inertial Navigation Systems (INSs) are assembled and be re-calibrated after certain periods of time. The multi-position calibration methods with advantage of not requiring high-precision equipment are widely discussed. However, the existing multi-position calibration methods for IMU are based on the model of linear scale factors. To improve the precision of INS, the nonlinear scale factors should be calibrated accurately. This paper proposes an optimized multi-position calibration method with nonlinear scale factor for IMU, and the optimal calibration motion of IMU has been designed based on the analysis of sensitivity of the cost function to the calibration parameters. Besides, in order to improve the accuracy and robustness of the optimization, an estimation method on initial values is presented to solve the problem of setting initial values for iterative methods. Simulations and experiments show that the proposed method outperforms the calibration method without nonlinear scale factors. The navigation accuracy of INS can be improved by up to 17% in lab conditions and 12% in the moving vehicle experiment, respectively.

A robust cascaded strategy of in-motion alignment for inertial navigation systems

Inertial navigation system needs to be initialized through the alignment process, before the transition into the navigation stage can be made. In this article, a robust cascaded strategy of alignment which aims to provide an automatic operating alignment strategy for different application scenarios is proposed. The robust cascaded strategy of alignment utilizes the advantages of several alignment methods to form a cascaded alignment strategy. As a result, the robust cascaded strategy of alignment can be utilized in applications with different grade inertial measurement units under complex dynamic condition. In addition, several control measures are added into the robust cascaded strategy of alignment process to increase its robustness and to ensure that acceptable performance may be attained. A filed test using two inertial measurement units (tactical-grade FSAS and micro-electro-mechanical-system-grade SBG) shows that the proposed robust cascaded strategy of alignment can achieve alignment under various dynamic conditions, such as low speed motion, turns, stationary positions, and straight motion. The mean heading error and level angle error are 0.10° and −0.08° for the FSAS, respectively, and −0.44° and −0.02° for the SBG, respectively. The root mean square of the heading error and level angle error for the FSAS are 2.08° and 0.50°, respectively, while those for the SBG are 2.95° and 1.44°, respectively.

PUSHBROOM HYPERSPECTRAL IMAGING FROM AN UNMANNED AIRCRAFT SYSTEM (UAS) – GEOMETRIC PROCESSINGWORKFLOW AND ACCURACY ASSESSMENT

Abstract. In this study, we assess two push broom hyperspectral sensors as carried by small (10–15 kg) multi-rotor Unmanned Aircraft Systems (UAS). We used a Headwall Photonics micro-Hyperspec push broom sensor with 324 spectral bands (4–5 nm FWHM) and a Headwall Photonics nano-Hyperspec sensor with 270 spectral bands (6 nm FWHM) both in the VNIR spectral range (400–1000 nm). A gimbal was used to stabilise the sensors in relation to the aircraft flight dynamics, and for the micro-Hyperspec a tightly coupled dual frequency Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU), and Machine Vision Camera (MVC) were used for attitude and position determination. For the nano-Hyperspec, a navigation grade GNSS system and IMU provided position and attitude data. This study presents the geometric results of one flight over a grass oval on which a dense Ground Control Point (GCP) network was deployed. The aim being to ascertain the geometric accuracy achievable with the system. Using the PARGE software package (ReSe – Remote Sensing Applications) we ortho-rectify the push broom hyperspectral image strips and then quantify the accuracy of the ortho-rectification by using the GCPs as check points. The orientation (roll, pitch, and yaw) of the sensor is measured by the IMU. Alternatively imagery from a MVC running at 15 Hz, with accurate camera position data can be processed with Structure from Motion (SfM) software to obtain an estimated camera orientation. In this study, we look at which of these data sources will yield a flight strip with the highest geometric accuracy.

High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders.

APPLICATION OF VEHICLE DYNAMIC MODELING IN UAVS FOR PRECISE DETERMINATION OF EXTERIOR ORIENTATION

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

APPLICATION OF VEHICLE DYNAMIC MODELING IN UAVS FOR PRECISE DETERMINATION OF EXTERIOR ORIENTATION

Abstract. Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

Structural integrity verification of cable stayed footbridge based on FEM analyses and geodetic surveying techniques

This paper presents the mathematical models developed for the design of a pedestrian, cable stayed bridge, the geodetic monitoring procedures and their detailed cross-comparisons and analyses undertaken during the phases of construction and at commissioning stage. Because of the asymmetric design, the relative flexibility of the pylons, the eccentric positioning of the central joint and the off-plane low angles of the cables, a multi-sensor, event based geodetic monitoring scheme was adopted to assess the structural integrity of the bridge. During construction, the bridge kinematics were measured along both sides of the deck and at selected points on the pylon facades using a digital level and a high accuracy total station used in metrology respectively. At a commissioning stage, a number of load test series were undertaken using conventional geodetic techniques and a tactical grade Inertial Measurement Unit (IMU). Analysis confirmed the overall mathematical modelling assumptions made for the individual stages of construction and for the completed structure. However, analysis also revealed the increased structural rigidity of the structure. This particularly applies for the torsional stiffness of the deck that exhibits low (by a factor of 0·5) rotation angles compared to those obtained from the analytical models.