Navigation Errors Research Articles

LIO-Vehicle: A Tightly-Coupled Vehicle Dynamics Extension of LiDAR Inertial Odometry

We propose LIO-Vehicle, a new tightly-coupled vehicle dynamics extension of LiDAR inertial odometry (LIO) method that provides highly accurate, robust, and real-time vehicle trajectory estimation. Since most existing LiDAR-based localization methods are not specifically proposed for vehicles, they do not take the motion constraints of ground robots into account. And they may not work well in structure-less areas, such as tunnels and narrow corridors. For LIO in these LiDAR-degraded circumstances, inertial sensors alone are unable to sustain reliable long-term accuracy due to the accumulation of errors without external periodic corrections. Therefore, it is necessary to introduce other low-cost sensors and vehicle motion constraints to build a more accurate and robust odometry algorithm. In this letter, we use wheel speedometer and steering angle sensor measurements to establish a two-degree-of-freedom vehicle dynamics model and then construct a preintegration factor based on the model’s output. At the backend, we add the vehicle dynamics preintegration results, IMU preintegration measurements, and LiDAR odometry results to a factor graph and get the optimized result with the help of sliding window optimization. The experiments show that the proposed method can achieve higher positioning accuracy compared with the existing LiDAR inertial odometry methods and it can significantly mitigate navigation error in harsh areas where environmental features are insufficient for LiDAR odometry.

CKF 기반 상태 확장을 통해 IMU 의 장착 비정렬을 보상하는 차량용 복합항법시스템

The navigation information of autonomous vehicles is very important not only for determining the driving route but also for controlling the vehicle. Therefore, the navigation information must exhibit a high short-term/long-term stability and accuracy, and high output frequency. Accordingly, navigation systems are configured by integrating various sensors, and a representative inertial navigation system (INS)/global positioning system (GPS) integrated navigation system has been used. In tunnels and urban areas, however, the continuity of INS error correction is not guaranteed owing to the blocking and contamination of GPS signals, and the operation of a standalone INS increases navigation error. To address this problem, the construction of an INS/GPS/non-holonomic constraints (NHC) integrated navigation based on the NHC-based movement of the land vehicle has been utilized (that is, the information in which the lateral and vertical velocities of the vehicle are 0 was used). However, the misalignment of the sensing axis of IMU with the body coordinate system of the vehicle results in the collapse of the NHC condition, which increases the INS error. To address this problem, in this study, a navigation filter was designed to estimate/compensate not only the INS errors but also the mounting misalignment of the IMU by extending the state based on the cubature Kalman filter (CKF). In addition, the accuracy of navigation information according to whether NHC information was used or not and whether it is mixed with GPS measurements was verified based on Monte Carlo simulation.

Research on Innovative Self-Calibration Strategy for Error Parameters of Dual-Axis RINS

The rotational inertial navigation system (RINS) could greatly improve navigation accuracy by rotating the inertial measurement unit with gimbals. But it will also excite and amplify the corresponding error parameters, so it is more necessary to calibrate the typical error parameters accurately. Fortunately, the self-calibration strategy could be achieved conveniently in RINS. In this paper, an innovative self-calibration strategy for error parameters of dual-axis is proposed. The calibration strategy is designed that the inner and outer gimbals rotate at different angular speeds at the same time, and the whole calibration process lasts for 12 min. The relationship between the error parameters to be calibrated and the navigation error is derived, and the principle of error separation is analyzed, which proves the rationality of the strategy. The calibration strategy is verified by simulation and experiment, and the 21 error parameters of the system can be well estimated. The 3h static navigation experiment shows that the position error is less than 0.15nmile/h(CEP) after compensating the error parameters calibrated by the self-calibration strategy, and achieved the expected accuracy of the dual-axis RINS. Finally, it is proved that the calibration strategy has the obvious advantages of short calibration time and simple process, which is of great significance to improve the navigation accuracy of dual-axis RINS.

Skylight polarization patterns under urban obscurations and a navigation method adapted to urban environments

Owing to preferable anti-interference and anti-cumulative-error capabilities, polarized skylight navigation technology has been developed. However, in urban environments with extensive demand, the sky is usually partially obscured by buildings and trees. Urban landscape obscurations with polarization patterns that have not been sufficiently studied can greatly influence navigation accuracy. In this paper, we study the polarization patterns generated by obscurations and summarize the impacts of obscured urban sky scenes on the navigation results. We also propose a full-sky polarization imaging navigation method adapted to urban environments. A compact full-sky polarimeter is established, and a specific pattern inpainting algorithm based on convolution operation is proposed to amend the navigation errors caused by obscurations. Among 174 sets of comparative experiments, 90.2% of the extraction results are improved after inpainting, which verifies the effectiveness and robustness of the method. Discussions on the optimization of parameters in the algorithm and the recommended values are also provided. This work offers a novel approach to overcome the impacts of obscurations for urban polarization navigation.

Sequential convex programming approach for real-time guidance during the powered descent phase of mars landing missions

• The main contribution of this study is the development of a new sequential-convex-programming-based technique that can periodically update the real-time guidance solution in consideration of the minimum-fuel Mars powered descent guidance problem with free final time. • To illustrate the validity of the proposed strategy for on-board real-time applications, an on-board test was conducted using the GR740, which is the processor of choice for the next-generation on-board computers of the European Space Agency (ESA) and Korea Aerospace Research Institute (KARI). • To assess the performance of the strategy, numerical simulations were conducted under various conditions. • Furthermore, the results of Monte Carlo simulations revealed that the proposed strategy is adequately robust against environmental and systematic disturbances. An on-board guidance generation approach for Mars pinpoint landing has been developed based on sequential convex programming. To optimize the flight time in the formulation, a discretized form of the landing problem was constructed under the conditions of linearized states, controls, and time increment between consecutive time steps. To implement this strategy in real time, on-board demonstrations were conducted with the GR740, which is the next-generation on-board processor of choice for the European Space Agency. The total number of time steps was determined based on the results of these demonstrations. The numerically simulated results also indicate that the solution obtained via the proposed strategy is close to the optimized GPOPS-II solution under the condition of no disturbance. Even under disturbances such as navigation errors, initial prediction errors, and perturbation forces, the proposed strategy ensures that the spacecraft reaches its target position with near-optimal fuel consumption.

The accuracy of acetabular cup placement in primary total hip arthroplasty using an image-free navigation system

BackgroundIntraoperative navigation systems have been shown to improve the accuracy of acetabular component insertion in total hip arthroplasty (THA). The purpose of this study was to investigate the accuracy of cup orientation in primary THA using an image-free navigation system.MethodsA total of 107 consecutive cementless THAs using an image-free navigation system were performed from February 2017 to March 2020 (the navigation group). As a control group, 77 retrospective consecutive cases who underwent THAs with manual implant-techniques between February 2012 and April 2017 were included. Postoperative cup radiographic inclination and radiographic anteversion relative to the functional pelvic plane were assessed using a 3D-template system after computed tomography (CT) examination.ResultsThe mean absolute errors of the postoperative measured angles from the target angles in inclination were 3.4° ± 3.0° in the navigation group and 8.4° ± 6.6° in the control group (p < 0.001). The mean absolute errors in anteversion were 5.1° ± 3.6° in the navigation group and 10.8° ± 6.5° in the control group (p < 0.001). The percentage of cups inside the Lewinnek safe zone was 93% in the navigation group and 44% in the control group (p < 0.001). The mean absolute values of navigation error were 3.3° ± 2.8° in inclination and 5.8° ± 4.9° in anteversion. Among the cases of osteoarthritis, the inclination error was significantly higher in Crowe group 2 to 4 than in Crowe group 1 (5.1° ± 3.5° and 3.0° ± 2.5°, respectively, p < 0.05). The percentage of hips with inclination error over 10° in Crowe group 2 to 4 was significantly higher than in Crowe group 1 (17 and 1%, respectively, p < 0.05).ConclusionsThe image-free navigation system improved the accuracy of cup orientation. The accuracy of cup position was less in Crowe group 2 to 4 than in Crowe group 1.

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to reduce the attitude error and position drift (both horizontal and vertical) with the dual objective of improving the aircraft GNSS-Denied inertial navigation capabilities as well as facilitating the fusion of the inertial filter with visual odometry algorithms. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results, compare the proposed filter with more traditional implementations, and analyze the sensitivity of the results to the quality of the onboard sensors. The author releases the ▪ implementation of both the navigation filter and the high fidelity simulation as open-source software [1].

Semi-analytical assessment of the relative accuracy of the GNSS/INS in railway track irregularity measurements

An aided Inertial Navigation System (INS) is increasingly exploited in precise engineering surveying, such as railway track irregularity measurement, where a high relative measurement accuracy rather than absolute accuracy is emphasized. However, how to evaluate the relative measurement accuracy of the aided INS has rarely been studied. We address this problem with a semi-analytical method to analyze the relative measurement error propagation of the Global Navigation Satellite System (GNSS) and INS integrated system, specifically for the railway track irregularity measurement application. The GNSS/INS integration in this application is simplified as a linear time-invariant stochastic system driven only by white Gaussian noise, and an analytical solution for the navigation errors in the Laplace domain is obtained by analyzing the resulting steady-state Kalman filter. Then, a time series of the error is obtained through a subsequent Monte Carlo simulation based on the derived error propagation model. The proposed analysis method is then validated through data simulation and field tests. The results indicate that a 1 mm accuracy in measuring the track irregularity is achievable for the GNSS/INS integrated system. Meanwhile, the influences of the dominant inertial sensor errors on the final measurement accuracy are analyzed quantitatively and discussed comprehensively.

An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments.

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

Sensitivity of back‐projection algorithm (BPA) synthetic aperture radar (SAR) image formation to initial position, velocity, and attitude navigation errors

The Back-Projection Algorithm (BPA) is a time-domain-matched filtering technique to form synthetic aperture radar (SAR) images. To produce high-quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real-data image formation.

An Improved Navigation Method of Inertial Navigation System for Three-Dimensional Positioning

An inertial navigation system (INS) that uses portable and service inertial measurement units (IMUs) can potentially obtain two-dimensional (2D) trajectories of pedestrians. The use of updated algorithms is useful in reducing drift accumulation errors of inertial sensors. This paper introduces an improved navigation technique based on the zero-velocity update (ZUPT) method in order to measure the displacement length. Meanwhile, the zero-height update (ZHUPT) and the modified heuristic drift reduction (HDR) methods are adopted to decrease measurement error within a 2D plane. Experimental results indicate that the navigation errors of 2D trajectories are below 2%. Furthermore, a height update method has been proposed for three-dimensional (3D) navigation that uses the actual slope angle of the stairs and can restrain the height divergence. The terminal height error accounts for 1.28% of the total altitude variation.

The orbitofrontal cortex maps future navigational goals

Accurate navigation to a desired goal requires consecutive estimates of spatial relationships between the current position and future destination throughout the journey. Although neurons in the hippocampal formation can represent the position of an animal as well as its nearby trajectories1–7, their role in determining the destination of the animal has been questioned8,9. It is, thus, unclear whether the brain can possess a precise estimate of target location during active environmental exploration. Here we describe neurons in the rat orbitofrontal cortex (OFC) that form spatial representations persistently pointing to the subsequent goal destination of an animal throughout navigation. This destination coding emerges before the onset of navigation, without direct sensory access to a distal goal, and even predicts the incorrect destination of an animal at the beginning of an error trial. Goal representations in the OFC are maintained by destination-specific neural ensemble dynamics, and their brief perturbation at the onset of a journey led to a navigational error. These findings suggest that the OFC is part of the internal goal map of the brain, enabling animals to navigate precisely to a chosen destination that is beyond the range of sensory perception.

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.

Real-time, model-aided ranging for small scale operations in the Beaufort Sea, an ice-covered, double ducted environment

This talk will cover operational methods and results for vehicle navigation from the Ice Exercise in March 2020 (ICEX20), in the Beaufort Sea. For short range acoustic transmissions, the total ice cover and the double ducted environment co-create complex multipath uncertainty. We assumed that the horizontal group velocity between source and receiver was smoothly varying, and embedded a “Virtual Ocean” with a real-time ray tracing engine to predict the horizontal group velocity for range estimation. This model-aided psuedorange approach, which enabled a successful vehicle recovery, favored the least multipath possible such that psuedoranges tended to overestimate the GPS-derived range by roughly 10 m. In post-processing, we modify the group velocity estimation method to consider varying degrees of multipath and achieve a mean absolute error of roughly 4 plus or minus 4 meters, rivaling GPS performance. To our knowledge, these results are the first field experiment to demonstrate a real-time, model-based data processing to constrain ranging error for underwater navigation. [Work supported by ONR & the NDSEG fellowship.]

Progress on error propagation and correction of long-range rockets in disturbing gravity field

Disturbing gravity field is becoming an important factor leading to impact error of long-range rockets. In this paper, the influence mechanism of deflection of the vertical and spatial disturbing gravity on inertial navigation and guidance system are firstly introduced, respectively. Then, the navigation error propagation methods due to disturbing gravity field are reviewed. The fast assignment models of disturbing gravity field, which are available for compensating navigation errors in engineering, are also summarized. After that, the unpowered trajectory error propagation methods and the corresponding guidance correction strategies, as well as potential directions for future efforts, are discussed.

Autonomous Landing of Micro Unmanned Aerial Vehicles with Landing-Assistive Platform and Robust Spherical Object Detection

Autonomous unmanned aerial vehicle (UAV) landing can be useful in multiple applications. Precise landing is a difficult task because of the significant navigation errors of the global positioning system (GPS). To overcome these errors and to realize precise landing control, various sensors have been installed on UAVs. However, this approach can be challenging for micro UAVs (MAVs) because strong thrust forces are required to carry multiple sensors. In this study, a new autonomous MAV landing system is proposed, in which a landing platform actively assists vehicle landing. In addition to the vision system of the UAV, a camera was installed on the platform to precisely control the MAV near the landing area. The platform was also designed with various types of equipment to assist the MAV in searching, approaching, alignment, and landing. Furthermore, a novel algorithm was developed for robust spherical object detection under different illumination conditions. To validate the proposed landing system and detection algorithm, 80 flight experiments were conducted using a DJI TELLO drone, which successfully landed on the platform in every trial with a small landing position average error of 2.7 cm.

Error Compensation of Strapdown Inertial Navigation System for the Boom-Type Roadheader under Complex Vibration

The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.

A hybrid particle swarm optimization-gauss pseudo method for reentry trajectory optimization of hypersonic vehicle with navigation information model

Reentry trajectory optimization of hypersonic vehicle has been a hotspot in recent years, and the existing methods suffer from two drawbacks. First, the navigation error caused by the blackout zone is not considered in the reentry trajectory optimization model. Second, a single approach is usually applied to optimize the reentry trajectory, which fails to cover the shortage of it by combining with other approaches. To this end, a hybrid particle swarm optimization (PSO)-gauss pseudo method (GPM) algorithm, namely the hybrid PSO-GPM algorithm, is proposed to deal with the reentry trajectory optimization problem in this paper. The navigation information model reflecting the influence of the blackout zone on the global positioning system (GPS)/inertial navigation system (INS) is established first. In this model, the states of hypersonic vehicle are represented by random values obeying the normal distribution rather than the determined values, and the standard deviation is calculated from the error principle of INS. In the hybrid PSO-GPM algorithm, GPM works in the inner loop to solve the reentry trajectory optimization problem with a fast convergence and high precision under a provided initial guess. PSO plays a role in the outer loop to optimize the initial guess for GPM. Simulation results demonstrate that the established navigation information-based reentry trajectory optimization model is rational and can improve the safety level of flight. With the hybrid PSO-GPM algorithm, a better solution can be generated compared to the results when PSO algorithm and GPM are used separately.

Low-cost positioning performance using remotely sensed ionosphere

Low-Cost Positioning [Single Frequency Receivers (SFR)] using Global Positioning Systems (GPS) can become a precise alternative to Dual Frequency Receivers (DFRs) in different applications. Ionospheric delay is one of the significant provenances of errors in GPS navigation and positioning. These blunders in both Phase range and pseudo range can differ according to the receiver location, solar cycle, time, and geomagnetic activity.Some ground receivers over Egypt and Europe were regarded in This publication to examine various ionosphere correction products' capability. Data from IGS stations spanning various latitudes and solar activity times (high, medium, or small) are taken into account. The findings demonstrate that the sub-meter level's precision is obtainable using single-frequency data using suitable mitigation approaches for ionospheric impacts.The implications for GNSS positioning (especially on horizontal precision) of ionospheric corrections have been studied. The findings demonstrate that all ionosphere corrections clearly enhance the accuracy of the vertical positions where the absolute vertical error is about 30 cm, 32.3 cm, and 57.2 cm for LIMs (Local Ionosphere Maps), GIMs (Global Ionosphere Maps), and BKP (Broadcasted Klobuchar Parameter). However, the horizontal enhancement is lower than the vertical, and even worse, the horizontal error can be increased with ionosphere correction. The horizontal error ranges from 2.7 to 51.8 cm by using LIMs, while ranges from 4.5 to 47.2 cm by using GIMs but the maximum horizontal error obtained by applying BKP, which ranges from 20.7 to 100.2 cm. In comparison, the north bias plays a vital role in reducing horizontal precision. If north bias were not resolved correctly with ionosphere corrections, the horizontal positioning efficiency would deteriorate.The significant efficiency of mid-latitude Egyptian ionosphere correction (estimated by LIMs) in stormy and quiet geomagnetic environments rather than GIMs and BKP, 3D RMS error improved by 16 %, and GIMs up 63 %. Furthermore, in low solar activity, 3D RMS error improved 6% compared to GIMs and 87% compared to BKP.

Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.

Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in automatic modes is undoubtedly expected. In this paper, an autonomous disinfection vehicle with an Ultraviolet-C (UVC) lamp is designed and implemented using an ultra-wideband (UWB) positioning sensor. The UVC dose for 90% inactivation of the reproductive ability of COVID-19 is 41.7 J/m2, which a 40 W UVC lamp can achieve within a 1.6 m distance for an exposure time of 30 s. With this UVC lamp, the disinfection vehicle can effectively sterilize in various scenarios. In addition, the high-accuracy UWB positioning system, with the time difference of arrival (TDOA) algorithm, is also studied for autonomous vehicle navigation in indoor environments. The number of UWB tags that use a synchronization protocol between UWB anchors can be unlimited. Moreover, this proposed Gradient Descent (GD), which uses Taylor method, is a high-efficient algorithm for finding the optimal position for real-time computation due to its low error and short calculating time. The generalized traversal path planning procedure, with the edge searching method, is presented to improve the efficiency of autonomous navigation. The average error of the practical navigation demonstrated in the meeting room is 0.10 m. The scalability of the designed system to different application scenarios is also discussed and experimentally demonstrated. Hence, the usefulness of the proposed UWB sensor applied to UVC disinfection vehicles to prevent COVID-19 infection is verified by employing it to sterilize indoor environments without human operation.