Research on A Low Computational Cost Vision-aided Inertial Navigation Method for Precision Landing on Asteroid

With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.

Due to melting ice at the North Pole, navigation at high latitudes is increasing regularly. Therefore, navigation and survey at these high latitudes becomes more and more a key issue, especially for underwater operations where GNSS signals are not available. Navigation at high latitudes is a challenge for inertial navigation systems. Alignment of these systems with respect to North becomes more difficult when closer to the pole because the physical effect that is used becomes smaller and smaller when going to high latitudes. In order to evaluate heading accuracy and navigation performance of its inertial systems under these extreme conditions, iXBlue had the opportunity to test several of its navigation systems on a vessel up to 81 degrees of latitude during summer 2014. Thanks to the collaboration of the Norwegian Polar Institute, iXBlue has tested QUADRANS gyrocompass and PHINS navigation systems on the research Vessel Lance. The paper emphasizes on the potential difficulties encountered by inertial navigation systems at high latitudes. In a second part the paper presents and analyzes performance of all navigation systems and gyrocompasses under test in terms of heading accuracy and long term stability and navigation accuracy without GNSS aiding. The third part explains why we obtained extremely good results during this test campaign.

Rotation modulation technology could effectively improve the accuracy of the inertial navigation system (INS) by compensating for the biases of the inertial sensors automatically. However, the carrier angular motion and rotation control error could reduce the rotation modulation effect and then decrease the navigation accuracy. To address this problem, for the single-axis rotation INS, a novel rotation control scheme is presented. The control scheme employs the fiber optic gyros to control the inertial measurement unit (IMU) rotation angular velocity so that the INS with both rotation modulation and azimuth motion insulation functions. Furthermore, in order to reduce the control error, this study adopts two ways: optimizing the control strategy and shortening the delay time. The former way is to control the IMU rotating about the z-axis of the platform frame with respect to the navigation frame, rather than the up-axis of the navigation frame. The latter way is to apply interrupt mode rather than inquiry mode to complete the data transfer between the navigation and the control processors. The simulation and experimental results demonstrate that: the proposed method would not only realize the rotation modulation of the biases of the inertial sensors, but also achieve the insulation of the azimuth motion. The steady-state control error of the control system is less than 10" and the overshoot control error is less than 50". Compared to the traditional SRINS, the navigation position error in the single-axis rotation/azimuth-motion insulation INS could reduce 50% in some navigation application.

This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.

The autonomous vehicle navigation system is heavily dependent on the use of GPS sensors. The poor accuracy of GPS sensors can be circumvented by combining other sensors using a Kalman filter-based method. Combining GPS and IMU sensors or other sensors is an option because of the relatively low cost. The use of controllers to build an autonomous vehicle navigation system becomes an important thing, so the controller devices used must have excellent performance, and it is found in UA V technology with autopilot-based controllers. The sensor combination method is carried out by combining the IMU sensor on the pixhawk 2.1 controller module with the Here2 GPS/GNSS Module, which is expected to make autonomous vehicles' position and heading orientation more accurate. The initial test results show that the navigation system accuracy is very good with the orientation angle towards the vehicle as expected. However, there was a problem in sending data from the vehicle to the ground station via telemetry radio due to distance and buildings constraints.

Navigation systems for ships mainly include inertial navigation system (INS), “BeiDou” satellite navigation system, Global Positioning System (GPS), and Doppler navigation system and so on. The characteristics of each system are different. In order to improve the accuracy and fault-tolerant ability of naval-ship navigation system, an integrated navigation algorithm based on Measurement-based adaptive Kalman filtering algorithm (MAKF), improved Suge-Husa algorithm and non-reset federated Kalman filter structure is proposed in this paper. The algorithm uses the short-term high precision of the inertial navigation system and the prior velocity information of the ship to adaptively estimate the measurement noise characteristics of the satellite navigation system. When the satellite navigation system is unable to locate, the INS and the log are combined with the help of the prior velocity information. The simulation results show that the algorithm can effectively exploit the characteristics of each navigation system, and can output high-precision navigation results even when the GPS/BeiDou satellite navigation system fails.

Beidou satellite navigation system (BDS) signal is blocked during dynamic positioning which can lead to poor geometry structure of visible satellites and serious multipath effect. In this contribution, the advantages of inertial navigation system (INS), autonomy and short-term accuracy, are used to speed up the BDS ambiguity resolution (AR). However, the accuracy of general INS cannot meet the fixed BDS original carrier ambiguity requirement. BDS can broadcast three frequency observations, so the virtual observations of long wavelength and short wavelength are formed by linear combination, which make the INS precision meet the fixed ambiguity requirement and achieve high precision positioning, respectively. The short wavelength observations ambiguity due to the relatively large noise is difficult to reliably fixed. To solve this problem, Empirical mode decomposition (EMD) can be used to decompose the error of the short wavelength observations into two parts: systematic error and random error. Systematic error is used to correct the short wavelength ambiguity float solution and reduce the ambiguity search space, to achieve a reliable fixed purpose, finally solving the baseline solution. The experimental results show that the algorithm is more fast and reliable than the traditional algorithms, and more suitable for dynamic positioning in the complex environment.

Initiated and financed by the Canadian Space Agency, Micro-Rover Platform with Tooling Arm (MRPTA) project has targeted the development of a robotic system with remote control and autonomous navigation capabilities for testing a large variety of analogous planetary missions. Although developed for planetary exploration, the navigation system is highly suitable to such areas of application as construction and in mining. Autonomous navigation system is capable of moving the platform to predefined position(s)/orientation(s) while continuously mapping the terrain, assessing its traversability and choosing most appropriate path to follow. Designed according to the JAUS (Joint Architecture for Unmanned Systems) framework, the system consists of several interconnected components. Following components are of particular importance: Pose Estimator (PE), Terrain Evaluator (TE), Map Manager (MP), and Path Planner (PP). The PE uses a minimal sensor configuration consisting of an azimuth gyroscope, inclinometer and wheel odometry. The PE provides reliable pose estimates in spite of pronounced slippage by employing extensive use of fused data. The TE uses a nodding laser scanner which continuously sweeps the area in front of the platform and constructs a “traversability grid” (TG) of the surrounding area. Multiple TGs are constantly constructed along the platform motion path are combined into a terrain map maintained by the MP. The PP uses this map to compute a motion path Obtained path is executed by the motion controller and remains current until it is contradicted by the most recent map – at which point it is re-planned. This paper addresses integration issues, lessons learned and also outlines the possible applications for construction and mining.

Submarines in the underwater sailing need a safe, reliable, high accurate, and covert well navigation system. Inertial navigation system (INS) is the core of underwater navigation. But the inertial navigation system gathers information based gyroscope, accelerometer and other sensors. In accordance to Newton's laws of mechanics, their own speed, location and other information is calculated by integral recursion. Since the recursive work of INS, positioning error gradually increases with time elapsing. Gravity and gravity gradient aided navigation as a passive autonomous navigation are more and more focused on, Selection of the gravity gradient matching area is one of the key to gravity gradient matching navigation. Earth's marine area is enormous, underwater environment is complex. Take advantage of multi-feature information fusion of gravity gradient full tensor, one hand a wider range of matching area can be got, to gain wider path planning area. on the other hand, the positioning accuracy of assisted navigation system can be inproved.

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities (10s of g and 1000s of deg/sec) during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer’s signals with a triangular function and estimates the size of the triangles using a Gaussian Process (GP) regression. To evaluate performance of pedestrian navigation systems enhanced by the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU, which have accelerometer’s FSR of ±16 g and ±40 g, respectively. In the first series of experiments, forces experienced by the foot-mounted IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer’s readings. The saturated accelerometer’s readings reduced the accuracy of estimated positions using the VN-200 by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.34}\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${3.37}\times $ </tex-math></inline-formula> along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated accelerometer’s measurements, the navigation accuracy along horizontal and vertical directions was increased by 5% and 50%, respectively, as compared to using unreconstructed signals.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

Computer-assisted navigation systems for hip resurfacing arthroplasty are designed to minimize the chance of implant malposition. However, there is little evidence computer navigation is useful in the presence of anatomical deformity. We therefore determined the accuracy of an image-free resurfacing hip arthroplasty navigation system in the presence of a pistol grip deformity of the head and femoral neck junction and of a slipped upper femoral epiphysis deformity. We constructed an artificial phantom leg from machined aluminum with a simulated hip and knee. The frontal and lateral plane implant-shaft angles for the guide wire of the femoral component reamer were calculated with the computer navigation system and with an electronic caliper combined with micro-CT. There was a consistent disagreement between the navigation system and our measurement system in both the frontal plane and lateral plane with the pistol grip deformity. We found close agreement only for the frontal plane angle calculation in the presence of the slipped upper femoral epiphysis deformity, but calculation of femoral head size was inaccurate. The use of image-free navigation for the positioning of the femoral component appears questionable in these settings.

This paper presents a method to extract heading information from the sky light polarization pattern and successfully implements the polarized light compass (PL-compass) under foliage environment with heavy occlusions for the first time. A classifier based on the primal support vector machine is trained to detect the sky area, and the random sample consensus algorithm is used to choose the inliers among the sky area. The sun vector is built upon the valid measurements. The attitude angles (Roll and Pitch) from the inertial navigation system (INS) are used to establish the Zenith direction when the platform tilts during traveling. The results show that the PL-compass can provide accurate heading angle (RMSE = 1.58°) by seeing only a tiny part of the skylight (minimum of 0.028%) in the dynamic vehicle test. The PL-compass is also integrated with the visual INS based on the indirect extended Kalman filter to improve the navigation accuracy. Each time a new measurement becomes available, the states estimated by the INS are used to check the consistency of the innovation and reject the outliers. The results show that the position errors of the integrated navigation system are greatly reduced with the help of the PL-compass. The estimated trajectory tracks the ground truth very well. The position error is 25.9 m (1.03% of the distance traveled) with heading errors less than 1° in the experiment.