Inertial Navigation Algorithm Research Articles

Vision-aided inertial navigation for planetary landing without feature extraction and matching

In this paper, a vision-aided inertial navigation method for planetary landing based on the direct method is proposed. This approach requires only a monocular camera and an inertial measurement unit (IMU) to be equipped on the lander. Unlike traditional feature-based visual-aided inertial navigation algorithms, which entail extracting and matching image features and thus incurring high computational costs, the proposed method utilizes the direct method that tracks any pixel with a sufficient intensity gradient. By jointly optimizing photometric and inertial errors, the proposed algorithm estimates the camera motion and sparse map of the scene. Simulation results demonstrate that the proposed method reduces computation by nearly 65 % compared to the feature point method, with a slight decrease in navigation accuracy.

Investigation of Enhanced Inertial Navigation Algorithms by Functional Iteration

Investigation of Enhanced Inertial Navigation Algorithms by Functional Iteration

Long-Distance GNSS-Denied Visual Inertial Navigation for Autonomous Fixed-Wing Unmanned Air Vehicles: SO(3) Manifold Filter Based on Virtual Vision Sensor

This article proposes a visual inertial navigation algorithm intended to diminish the horizontal position drift experienced by autonomous fixed-wing UAVs (unmanned air vehicles) in the absence of GNSS (Global Navigation Satellite System) signals. In addition to accelerometers, gyroscopes, and magnetometers, the proposed navigation filter relies on the accurate incremental displacement outputs generated by a VO (visual odometry) system, denoted here as a virtual vision sensor, or VVS, which relies on images of the Earth surface taken by an onboard camera and is itself assisted by filter inertial estimations. Although not a full replacement for a GNSS receiver since its position observations are relative instead of absolute, the proposed system enables major reductions in the GNSS-denied attitude and position estimation errors. The filter is implemented in the manifold of rigid body rotations or SO(3) in order to minimize the accumulation of errors in the absence of absolute observations. Stochastic high-fidelity simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results. The authors release the C++ implementation of both the visual inertial navigation filter and the high-fidelity simulation as open-source software.

Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.

Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31-134× smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.

An INS/Geomagnetic Integrated Navigation Method for Coarse Estimation of Positioning Error and Search Area Adaption Applied to High-Speed Aircraft

The geomagnet aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft navigation. It can overcome the shortcomings of pure inertial navigation error accumulation over time and achieve long-term, autonomous, and all-weather navigation. The traditional iterative closest contour point (ICCP) algorithm, however, requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this article proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the aircraft flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this article can reach within 200 m.

RBCN-Net: A Data-Driven Inertial Navigation Algorithm for Pedestrians

Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.

Novel attitude determination method by integration of electronic level meter, INS, and low-cost turntable for level attitude evaluation and calibration of INS

Attitude references are greatly needed for the evaluation and calibration of Inertial Navigation Systems (INSs), which are widely used in gravimeter, marine, and aeronautical navigation. High-accuracy turntable, INS, and Global Navigation Satellite System have been utilized to verify the performance of relatively low-accuracy INS. The accuracy requirement of the attitude reference continuously increases with the rapid improvement of inertial sensors and navigation algorithms. However, the cost of attitude determination system increases rapidly with the increase of attitude accuracy requirement. To solve this limitation, the integration of level meter, INS, and low-cost turntable is proposed to provide level attitude, such as roll and pitch. The turntable is utilized to rotate the INS. An integration model of the level meter and INS is built to estimate the level attitude and reduce the cost of the turntable. The proposed method successfully avoids the dependence on high-accuracy turntables. An observability degree analysis is conducted to improve the level attitude accuracy further. The simulation and turntable test results indicate that the proposed method can provide high-accuracy level attitude without high-accuracy INS or turntable and is applicable to error calibration and attitude evaluation of INS.

Research on a High-Precision Measurement Algorithm Using FOG-Based Single-Axis RINS for Core Drilling

It is much important for core drilling to measure the orientation and trajectory of the drill bit precisely. Presently, with the higher and higher accuracy requirements of orientation and positioning, the utilization of inertial navigation system, especially the use of fiber optic gyroscope (FOG), has become a general trend. However, the realization of high precision and small size simultaneously is still a difficult challenge. In this paper, a FOG-based single-axis rotational inertial navigation system (RINS) instrument was used to suppress the inertial sensor errors automatically through rotation modulation technology. Based on the limited movement of the drill bit in lateral and vertical directions, this paper proposed a new dead reckoning (DR) algorithm which performed much better than normal inertial navigation algorithm due to the reduction of error sources. But the actual accuracy of DR algorithm was affected by the installation errors between the axes of the instrument and the pipeline. Therefore, this paper further proposed a new and practical calibration method of the installation errors to improve the accuracy of DR algorithm. To verify the rationality and the validity of the algorithm, the simulations and semi-physics simulation experiments were conducted under laboratory conditions. The results showed that the proposed DR algorithm with installation errors corrected could reach a high positioning accuracy of 0.5% utilizing FOGs with a drift of 0.015°/h. This study provided an innovative solution for high positioning precision based on a small-sized and low-cost RINS instrument in core drilling.

A novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment

The relative motion measurement has become the key technology for high precision imaging of array Synthetic Aperture Radar (SAR), which can be obtained by distributed Position and Orientation System (POS). The traditional method based on absolute parameters matching transfer alignment can measure the relative motion, but it will be affected by the main-system motion error and reduce precision. To solve this problem, a novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment is proposed. Firstly, the relative inertial navigation algorithm is used to preliminarily measure the relative motion. Then, the transfer alignment model based on relative parameters matching is established to rectify the inertial measured error and obtain the high-precision relative motion. Finally, experimental results show the proposed method is effective to isolate the main-system motion error and improve relative motion measurement accuracy compared to the traditional method.

A Human Gait Tracking System Using Dual Foot-Mounted IMU and Multiple 2D LiDARs.

This paper proposes a human gait tracking system using a dual foot-mounted IMU and multiple 2D LiDARs. The combining system aims to overcome the disadvantages of each single sensor system (the short tracking range of the single 2D LiDAR and the drift errors of the IMU system). The LiDARs act as anchors to mitigate the errors of an inertial navigation algorithm. In our system, two 2D LiDARs are used. LiDAR 1 is placed around the starting point, and LiDAR 2 is placed at the ending point (in straight walking) or at the turning point (in rectangular path walking). Using the LiDAR 1, we can estimate the initial headings and positions of each IMU without any calibration process. We also propose a method to calibrate two LiDARs that are placed far apart. Then, the measurement from two LiDARs can be combined in a Kalman filter and the smoother algorithm to correct the two estimated feet trajectories. If straight walking is detected, we update the current stride heading and the foot position using the previous stride headings. Then, it is used as a measurement update in the Kalman filter. In the smoother algorithm, a step width constraint is used as a measurement update. We evaluate the stride length estimation through a straight walking experiment along a corridor. The root mean square errors compared with an optical tracking system are less than 3 cm. The performance of proposed method is also verified with a rectangular path walking experiment.

Customizable Stochastic High-Fidelity Model of the Sensors and Camera Onboard a Fixed Wing Autonomous Aircraft.

The navigation systems of autonomous aircraft rely on the readings provided by a suite of onboard sensors to estimate the aircraft state. In the case of fixed wing vehicles, the sensor suite is usually composed by triads of accelerometers, gyroscopes, and magnetometers, a Global Navigation Satellite System (GNSS) receiver, and an air data system (Pitot tube, air vanes, thermometer, and barometer), and it is often complemented by one or more digital cameras. An accurate representation of the behavior and error sources of each of these sensors, together with the images generated by the cameras, is indispensable for the design, development, and testing of inertial, visual, or visual–inertial navigation algorithms. This article presents realistic and customizable models for each of these sensors; a ready-to-use C++ implementation is released as open-source code so non-experts in the field can easily generate realistic results. The pseudo-random models provide a time-stamped series of the errors generated by each sensor based on performance values and operating frequencies obtainable from the sensor’s data sheets. If in addition, the simulated true pose (position plus attitude) of the aircraft is provided, the camera model generates realistic images of the Earth’s surface that resemble those taken with a real camera from the same pose.

A Trident Quaternion Framework for Inertial-Based Navigation Part I: Rigid Motion Representation and Computation

Strapdown inertial navigation research involves the parameterization and computation of the attitude, velocity, and position of a rigid body in a chosen reference frame. The community has long devoted to finding the most concise and efficient representation for the strapdown inertial navigation system (INS). The current work is motivated by simplifying the existing dual quaternion representation of the kinematic model. This article proposes a compact and elegant representation of the body's attitude/velocity/position, with the aid of a devised trident quaternion tool, which is an extension of dual quaternions. And the position is accounted for by adding a second dual part to the dual quaternion. Eventually, the kinematics of the strapdown INS are cohesively unified in one concise differential equation, which bears the same form as the classical attitude quaternion differential equation. In addition, the numerical solution to this trident quaternion-based kinematic equation is dealt with the recently proposed functional iterative integration approach. Numerical results are provided to verify the analysis. The new representation not only provides an elegant kinematic form of inertial navigation, but gives birth to precision inertial navigation algorithm.

Design and verification of small solid rockets navigation algorithm test platform

Aiming at the navigation system of small solid rockets, a navigation algorithm test platform with real time simulator and flight control computer is designed. The inertial navigation algorithm is taken as an example to test the effectiveness of the platform. The flight dynamic model of a small solid rocket is established in a real time simulator, and the test is carried out in the working environment of a small test rocket to simplify the model. The acceleration and angular velocity data generated by the simulation is sent to the onboard computer to calculate the attitude of the rocket in real time, which verifies the correctness of the data communication link, real time solution and navigation algorithm between the onboard computer and the real time simulator. The test results show that the test platform is easy to use and can test the rocket navigation algorithm quickly and effectively.

PEDESTRIAN INERTIAL NAVIGATION ALGORITHM BASED ON SCENE RECOGNITION

Abstract. As indoor location-based services become increasingly essential to people's daily life, it is necessary to build a stable and accurate indoor pedestrian positioning system. The foot-mounted inertial navigation system can provide a short-term robust solution but suffer from error accumulation over time. To alleviate this issue, this paper proposes a pedestrian inertial navigation algorithm based on scene recognition to reduce the heading drift. Based on the hypothesis that corridors in buildings are generally narrow and straight and pedestrians have a high probability to walk in straight lines in corridors, we use a scene recognition model to assist foot-mounted INS. When the scene recognition model determines that the pedestrian is walking in a corridor, a straight-line constraint will be implemented to reduce the heading drift and improve the observability of the vertical gyroscope bias. Experiments show that the algorithm can effectively improve the navigation performance and the observability of vertical gyroscope bias when the sensor biases are significant.

Personnel Positioning System Method in Building Based on Inertial Sensor

At present, the global positioning system has been widely used in outdoor positioning and navigation, but in buildings, thick walls made of cement and bricks have a certain blocking effect on satellite signals, resulting in severely weakened GPS signals, and also extremely it may have an impact on the positioning accuracy, so that the positioning task cannot be completed. Inertial sensor is a kind of sensor mainly detecting and measuring acceleration tilt impact vibration rotation and multi-degree-of-freedom motion, is an important part to solve navigation orientation and motion carrier control. Therefore, we need to study the positioning system and positioning method of the people in the building according to the indoor positioning technology. This article mainly uses inertial sensors as indoor positioning technology for related research. This technology mainly includes linked inertial navigation algorithm and pedestrian dead reckoning algorithm. This paper uses pedestrian dead reckoning algorithm to study the positioning system. The research shows that the PDR algorithm designed this time is more accurate for the positioning of pedestrian movement in the scene of the building. Compared with the previous algorithm research, it has increased by 1%-2% accuracy meets the expected accuracy requirements.

New Zero-Velocity Detector for Low-Cost Inertial Pedestrian Navigation

A new Zero Velocity Interval (ZVI) detector is proposed and investigated in this article to augment the computation of a pedestrian’s position. In a low-cost pedestrian navigation system, the position of a pedestrian can be computed by using inertial navigation algorithms. The low-cost pedestrian navigation system employs an inertial measurement unit (IMU) comprised of accelerometer and gyroscope sensors to record acceleration and attitude rate. These measurements, when integrated mathematically, yield velocity and position. Similarly, attitude rate changes to attitude. The algorithm will then be able to figure out the changes in pedestrian position with the proper attitude. However, due to its low-cost nature, these sensors are built in such a way that their measurements are easily corrupted by noise, and once integrated mathematically, the measurement error grows exponentially. Zero Velocity Update (ZUPT) algorithm is frequently used to limit these errors. It works by detecting the ZVI that occur when the foot is on the ground. Assuming that the foot on the ground should have zero velocity, any remaining velocity measurements detected during this period are considered an error and are fed back to the navigation algorithm for correction. To detect the ZVI, a few commonly used detectors, namely Angular Rate Energy (ARE), Acceleration Moving Variance (MV), Acceleration Magnitude (MAG), and Generalized Likehood Ratio Test (GLRT) were revisited. These detectors were tested with real-world datasets of walking pedestrians. Then, a new detector, the Angular Rate Moving Variance (ARMV) detector, is proposed, and its performance is compared to that of the existing detectors.

A Robust Visual-Aided Inertial Navigation Algorithm for Pedestrians

Real-time and robust state estimation for pedestrians is a challenging problem under the satellite denial environment. The zero-velocity-aided foot-mounted inertial navigation system, with the shortcomings of unobservable heading, error accumulation, and poorly adaptable parameters, is a conventional method to estimate the pose relative to a known origin. Visual and inertial fusion is a popular technology for state estimation over the past decades, but it cannot make full use of the movement characteristics of pedestrians. In this paper, we propose a novel visual-aided inertial navigation algorithm for pedestrians, which improves the robustness in the dynamic environment and for multi-motion pedestrians. The algorithm proposed combines the zero-velocity-aided INS with visual odometry to obtain more accurate pose estimation in various environments. And then, the parameters of INS have adjusted adaptively via taking errors between fusion estimation and INS outputs as observers in the factor graphs. We evaluate the performance of our system with real-world experiments. Results are compared with other algorithms to show that the absolute trajectory accuracy in the algorithm proposed has been greatly improved, especially in the dynamic scene and multi-motions trials.

Analysis of Polar Region Navigation Algorithm of Laser Gyro Single-axis Rotation Modulation Inertial Navigation

In the polar regions, the local geographic frame will become invalid due to the convergence of meridians and other reasons. In response to this problem, the inertial navigation algorithm was reorganized in the transversal frame, and the error equation was also analyzed. The simulation experiment and actual experiment are carried out to verify the effectiveness of the inertial navigation algorithm in the transversal frame based on the single-axis rotation modulation inertial navigation system. Experimental results show that the transversal INS mechanization based on single-axis rotation modulation can solve the computational problems caused by the convergence of meridians in polar regions, achieving high-precision navigation.

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].

Pedestrian Navigation Method based on PDR/INS KF fusion and Height Update for Three-Dimensional Positioning

Inertial navigation system (INS) and pedestrian dead reckoning (PDR) that use wearable MEMS inertial measurement units (IMUs) can track the location of a pedestrian on two-dimensional (2D) plane. This paper proposes a pedestrian navigation method based on INS/PDR Kalman filter (KF) fusion to calculate the trajectory of a pedestrian in indoor corridors, which can effectively suppress the heading drift. Besides, the height update algorithm is introduced based on the pressure output of a barometer to constrain the height divergence for three-dimensional (3D) positioning. The results of motion experiment show that the navigation accuracy of INS/PDR Kalman filter fusion method is significantly increased compared with the INS. The proposed height update algorithm have better correction effect on the problem of height divergence compared with ZUPT-aided inertial navigation algorithm.