Integrated Navigation Method Research Articles

Integrated Navigation Method for Orchard-Dosing Robot Based on LiDAR/IMU/GNSS

To enhance the localization reliability and obstacle avoidance performance of the dosing robot in complex orchards, this study proposed an integrated navigation method using LiDAR, IMU, and GNSS. Firstly, the tightly coupled LIO-SAM algorithm was used to construct an orchard grid map for path planning and obstacle avoidance. Then, a global localization model based on RTK-GNSS was developed to achieve accurate and efficient initial localization of the robot’s coordinates and heading, and a Kalman filter was applied to integrate GNSS and IMU to improve robustness. Next, an improved A* algorithm was introduced to ensure the global operational path maintained a safe distance from obstacles, while the DWA algorithm handled dynamic obstacle avoidance. Field tests showed that the global localization model achieved an accuracy of 2.215 cm, with a standard deviation of 1 cm, demonstrating stable positioning performance. Moreover, the global path maintained an average safe distance of 50.75 cm from the obstacle map. And the robot exhibited a maximum absolute lateral deviation of 9.82 cm, with an average of 4.16 cm, while maintaining a safe distance of 1 m from dynamic obstacles. Overall, the robot demonstrated smooth and reliable autonomous navigation, successfully completing its tasks.

GNSS/IMU integrated navigation heading angle enhancement algorithm based on dual-antenna TDCP

In the integrated navigation system of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU), attitude estimation, especially accurate estimation of heading angle, is particularly important for real-time monitoring of vehicle driving status. However, since IMU is divergent in the altitude channel, its error will gradually accumulate if it is not accurately constrained. Therefore, the estimation accuracy of heading angle is insufficient in vehicle-mounted applications where the heading angle changes frequently. In order to solve the problem of poor navigation attitude estimation accuracy in the loose combination of traditional GNSS and IMU, this paper proposes a GNSS/IMU integrated navigation heading angle enhancement algorithm based on dual-antenna epoch carrier phase difference (TDCP). This method solves the vehicle heading angle through dual-antenna TDCP to increase the input dimension of the integrated navigation filter fusion observation value, and uses Hatch filtering and anti-error adaptive filtering to improve the observation domain pseudorange accuracy and GNSS/IMU integrated navigation positioning and attitude determination performance respectively. Experimental results show that compared with the traditional GNSS/IMU integrated navigation method, the proposed algorithm improves the positioning and speed measurement accuracy in the three-dimensional direction by and respectively 22.12%, 41.27%and the vehicle heading angle accuracy is improved 46.29%.

A Method for Assisting GNSS/INS Integrated Navigation System during GNSS Outage Based on CNN-GRU and Factor Graph

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.

Star angle modified with relativistic effects/StarNAV integrated navigation method for Mars exploration

The celestial navigation system based on star angle (SA) is a classical autonomous navigation method for the spacecraft, which directly provides the position information of the spacecraft relative to the near celestial body. But due to the relativistic effects, the star direction observed by spacecraft is inconsistent with that acquired from star ephemeris, which reduces navigation accuracy of SA. In addition, SA cannot directly provide the velocity information of the spacecraft. StarNAV is a novel celestial navigation method that utilizes the relativistic effects, which mostly provides the velocity information of the spacecraft. In this paper, the star angle modified with relativistic effects (SAMRE)/StarNAV integrated navigation method is proposed. The measurement model of SAMRE is established by considering relativistic effects in the measurement model of SA. Simulation results indicate that during the Mars approach phase, SAMRE has better navigation accuracy compared with SA, and the navigation accuracy of the SAMRE/StarNAV integrated navigation method is higher than that of SAMRE, StarNAV and SA/StarNAV, respectively. Furthermore, the paper analyses the impact of measurement errors on the navigation accuracy of SAMRE/StarNAV.

An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV's navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV's estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

A robust integrated navigation optimization method for USV in signal occlusion environment

Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

Non-Line-of-Sight Positioning Method for Ultra-Wideband/Miniature Inertial Measurement Unit Integrated System Based on Extended Kalman Particle Filter

In the field of unmanned aerial vehicle (UAV) control, high-precision navigation algorithms are a research hotspot. To address the problem of poor localization caused by non-line-of-sight (NLOS) errors in ultra-wideband (UWB) systems, an UWB/MIMU integrated navigation method was developed, and a particle filter (PF) algorithm for data fusion was improved upon. The extended Kalman filter (EKF) was used to improve the method of constructing the importance density function (IDF) in the traditional PF, so that the particle sampling process fully considers the real-time measurement information, increases the sampling efficiency, weakens the particle degradation phenomenon, and reduces the UAV positioning error. We compared the positioning accuracy of the proposed extended Kalman particle filter (EKPF) algorithm with that of the EKF and unscented Kalman filter (UKF) algorithm used in traditional UWB/MIMU data fusion through simulation, and the results proved the effectiveness of the proposed algorithm through outdoor experiments. We found that, in NLOS environments, compared with pure UWB positioning, the accuracy of the EKPF algorithm in the X- and Y-directions was increased by 35% and 39%, respectively, and the positioning error in the Z-direction was considerably reduced, which proved the practicability of the proposed algorithm.

Research on collaborative multi-UAV localization method based on combination navigation information

In challenging environments, unmanned aerial vehicle (UAV) systems often encounter unstable satellite signals and communication link interference. This paper proposes an integrated navigation method that integrates inertial navigation system (INS), global navigation satellite system (GNSS), and visual navigation system (VNS). Utilizing data from onboard sensors, this method merges relative navigation information from feature tracking of multiple UAVs with each UAV’s absolute navigation data. It includes specially designed transmission rules to reduce data exchange between UAVs. Each UAV uses an adaptive unscented Kalman filter (AUKF) method, which is enhanced into a collaborative AUKF (C-AUKF) using a message passing-based approach. Experiments in a simulated mission scenario revealed that the C-AUKF, in comparison to using extended Kalman filter (EKF), significantly improved flight test performance across the entire testing area, with a cumulative deviation of only 10.22 m, about 0.85% of the total flight distance. These results demonstrate that the proposed method not only meets accuracy requirements for position and velocity in integrated navigation but also significantly enhances multi-UAV navigation precision, particularly in scenarios with global positioning system (GPS) interference.

A SINS/DVL Integrated Navigation Method Based on EIMM-ARCKF Algorithm

A SINS/DVL Integrated Navigation Method Based on EIMM-ARCKF Algorithm

GNSS and LiDAR Integrated Navigation Method in Orchards with Intermittent GNSS Dropout

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.

Multi-AUV coverage path planning algorithm using side-scan sonar for maritime search

Coverage search is widely applied in the maritime military and civilian fields. To improve search efficiency and detection accuracy, we propose an effective and rapid multi-autonomous underwater vehicle (AUV) coverage path planning (ER-MCPP) algorithm using side-scan sonar (SSS). First, the given search region was optimized and modeled using grid decomposition. Next, a Voronoi diagram was used to divide and allocate the regions according to the initial position of the AUV. Subsequently, based on the idea of guidance, navigation, and control (GNC), a coverage search algorithm for a single AUV was proposed, including a path planning method combining improved lawn mower and outward-spiral methods, an integrated navigation method based on Kalman filtering, and a path tracking method considering ocean currents. In addition, we proposed a fault-tolerant control method. When an AUV fails unexpectedly, the other AUVs can accomplish a coverage search for the region through cooperation. Simulation experiments were performed using the MATLAB software. The results demonstrate that the proposed algorithm is valid. Compared with other methods, our algorithm has better robustness, finds targets more rapidly, and has a higher search efficiency. The practicality of the proposed algorithm is verified through field tests.

A Novel SINS/DVL Integrated Navigation Method Based on Different Track Models for Complex Environment

A Novel SINS/DVL Integrated Navigation Method Based on Different Track Models for Complex Environment

Time Dilation Modified-TDTOA/StarNAV Integrated Navigation Method for Mars Exploration

Time Dilation Modified-TDTOA/StarNAV Integrated Navigation Method for Mars Exploration

Doppler Velocity of the Sun/Star (DVS)-Aided SINS Integrated Navigation Method

Doppler Velocity of the Sun/Star (DVS)-Aided SINS Integrated Navigation Method

Overall integrated navigation based on satellite and lidar in the standardized tall spindle apple orchards

Overall navigation in orchards is essential for autonomous production activities, however, most navigation studies, in orchards, focus on in-row scenes, and few of them pay attention to the headland transition or the overall navigation. This paper proposes an integrated navigation method based on satellite and lidar for overall orchards scenes, where the lidar was mainly used for in-row navigation and chassis position (CP) obtaining, whilst the satellite was utilized for headland transition as well as the assistant for in-row integrated navigation. Two overall integrated navigation modes were investigated: CMD-INT, which integrates the command signals from the lidar-based and satellite-based navigation systems, and BIAS-FUS, which combines the information of bias from the two navigation systems. The CP was used to detect the headland of the tree rows and achieve overall navigation integration. A position-ratio-based path planning method was also proposed, which enabled the chassis to maintain a proper constant offset from one side of the orchard. The refined pure pursuit algorithm was adopted for path tracking with good adaptivity on both the path curvature and tracking velocity. Both simulation and field tests with ten group parameters configuration in total were conducted to verify the performance of the system. The BIAS-FUS mode outperformed the CMD-INT mode, and the findings for the simulation experiments were consistent with that of the field tests. In the simulation experiments, the maximum value (Max), mean value (Mean), and root mean square error (RMSE) for the in-row process decreased by 0.0054 m, 0.0053 m, and 0.0062 m, respectively. Additionally, the corresponding heading deviation decreased by 0.3027°, 0.0050°, and 0.0256°, respectively. In the field tests, the Mean of the heading and lateral deviation for the in-row tracking process decreased by 0.3823°, and −0.0072 m respectively, and the corresponding deviations for the transition tracking process decreased by 2.8903°, and 0.0164 m respectively. Aiming at the standardized tall spindle apple orchards, this work contributes to an available overall integrated navigation method.

Integration of SINS, laser doppler Velocimeter, and monocular visual odometry for autonomous navigation in complex road environments

For the integrated navigation method based on the strapdown inertial navigation system (SINS) and laser doppler velocimeter (LDV), the accuracy of the LDV is easily affected by the complex environment, such as sand and dust on the road, which leads to the degradation of the integrated navigation system’s positioning accuracy. In this paper, we propose an integrated navigation method for land vehicles that uses SINS, LDV, and monocular visual odometry (Mono-VO). We considered the boresight errors, lever-arm residual, and scale factor errors of Mono-VO when establishing an error model for the integrated SINS/LDV/Mono-VO navigation system. The new state vector’s observability is investigated. A Huber-based Kalman filter is also used to fuse these data. Finally, simulations and semi-physical simulations are carried out to validate the effectiveness of the proposed method, which demonstrates that positioning accuracy under complex road conditions is superior to that of the SINS/LDV navigation system.

A Comparative Study of Factor Graph Optimization-Based and Extended Kalman Filter-Based PPP-B2b/INS Integrated Navigation

Recently, factor graph optimization (FGO)-based GNSS/INS integrated navigation has garnered widespread attention for its ability to provide more robust positioning performance in challenging environments like urban canyons, compared to traditional extended Kalman filter (EKF)-based methods. In existing GNSS/INS integrated navigation methods based on FGO, the primary approach involves combining single point positioning (SPP) or real-time kinematic (RTK) with INS by constructing factors between consecutive epochs to resist outliers and achieve robust positioning. However, the potential of a high-precision positioning system based on the FGO algorithm, combining INS and PPP-B2b and that does not rely on reference stations and network connections, has not been fully explored. In this study, we developed a loosely coupled PPP-B2b/INS model based on the EKF and FGO algorithms. Experiments in different urban road and overpass scenarios were conducted to investigate the positioning performance of the two different integration navigation algorithms using different degrades of inertial measurement units (IMUs). The results indicate that the FGO algorithm outperforms the EKF algorithm in terms of positioning with the combination of GNSS and different degrades of IMUs under various conditions. Compared to the EKF method, the application of the FGO algorithm leads to improvements in the positioning accuracy of approximately 15.8%~45.9% and 19%~41.3% in horizontal and vertical directions, respectively, for different experimental conditions. In scenarios with long and frequent signal obstructions, the advantages of the FGO algorithm become more evident, especially in the horizontal direction. An obvious improvement in positioning results is observed when the tactical-grade IMU is used instead of the microelectron-mechanical system (MEMS) IMU in the GNSS/INS combination, which is more evident for the FGO algorithm than for the EKF algorithm.

A SINS/DVL navigation method based on hierarchical water velocity estimation*

Aiming at the problem that Doppler velocity log (DVL) cannot measure the velocity of the vehicle in the middle water environment, a Strap-down Inertial Navigation System (SINS)/DVL integrated navigation method based on hierarchical water velocity assistance is proposed. Firstly, according to the principle of DVL water layer flow measurement, the paper gives a hierarchical water recurrence principle. On this basis, a water velocity estimation method based on average weighted recurrence is proposed. The carrier depth information is introduced in this method, and the carrier velocity information is obtained by means of average value. This method can prevent the carrier velocity anomaly caused by the large error of single-layer water velocity measurement. Secondly, in view of the problem that the DVL water velocity measurement is easily disturbed in the middle water environment, a sliding window and a Chebyshev polynomial fitting function are introduced to realize the robust treatment of the water velocity. On this basis, combined with the Huber M robust filter algorithm, a hierarchical water velocity assisted integrated navigation algorithm based on the improved Huber M robust estimation is proposed to further improve the autonomous navigation accuracy in the middle water environment. Finally, the method proposed in this paper is verified by simulation. The results show that the hierarchical water velocity estimation method proposed in this paper can effectively estimate the water flow velocity, and its robust filtering algorithm can further improve the SINS/DVL integrated navigation accuracy in the middle water environment.

Fault-Tolerant SINS/Doppler Radar/Odometer Integrated Navigation Method Based on Two-Stage Fault Detection Structure.

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system.

Factor Graph with Local Constraints: A Magnetic Field/Pedestrian Dead Reckoning Integrated Navigation Method Based on a Constrained Factor Graph

The method of multi-sensor integrated navigation improves navigation accuracy by fusing various sensor data. However, when a sensor is disturbed or malfunctions, incorrect measurement information will seriously affect the estimation of the trajectory, which will lead to a decrease in accuracy. Existing factor graph models based on weights can neither fully resist the influence of disturbances nor guarantee the local rationality of estimated trajectories. In this paper, a factor graph with local constraints model that fuses the magnetic field and pedestrian dead reckoning data is proposed to navigate complex curved trajectories. First, adding local constraints to the pedestrian dead reckoning measurement converts the navigation solution problem into a hard-constrained nonlinear least squares problem. Then, a mapping model is constructed to reconstruct the variable space and the Adam gradient algorithm is used to realize a fast calculation. The navigation accuracy of this algorithm is better than that of the state-of-the-art method in real-world experiments, with an average accuracy of 0.83 m.