Integrated RTK/INS Navigation for Precision Agriculture

Lan, Haiyu; Elsheikh, Mohamed; Abdelfatah, Walid; Wahdan, Ahmed; El‐Sheimy, Naser

doi:10.33012/2019.17116

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…These, and the many other papers that use alternative but equivalent terminology, span a variety of approaches and applications, from terrestrial [ 4 , 5 , 6 ], to aeronautical [ 7 , 8 , 9 , 10 , 11 ], space [ 12 , 13 , 14 , 15 ]. In some instances, the search terms appear in reference to the technology being used for a specific application, e.g., precision agriculture [ 16 , 17 , 18 , 19 ] and construction [ 20 , 21 ]. In others, reference is made to implementation and algorithms.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

D’Adamo

Phillips

McĂree

2022

Sensors

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The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from 0.27m mean error to 0.06m when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to 0.14m when the pose estimator is operated in an a prior unknown environment.

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Section: Introductionmentioning

confidence: 99%

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

D’Adamo

Phillips

McĂree

2022

Sensors

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“…However, embedded magnetometers [20] are largely affected by magnetic disturbances, and their calibration is very challenging, especially for heavy vehicles moving at relatively low speeds, such as those expected for agricultural vehicles (around 3 km/h). Therefore, none of these solutions offers a reliable method of high accuracy for position and attitude estimation, as is needed in large grapevines or fruit plantations, where the rows are narrow and space is limited for maneuvers without the use of expensive sensors [21][22][23].The accuracy of consumer-grade IMUs is still limited because the estimated position and heading often have large cumulative errors over time, while they cannot self-determine the vehicle's initial state. Complex sensor calibration procedures are also required to improve the estimation of the heading angle [24] by reducing sensor drift [25].…”

Section: Introductionmentioning

confidence: 99%

RoboNav: An Affordable Yet Highly Accurate Navigation System for Autonomous Agricultural Robots

2022

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The paper presents RoboNav, a cost-effective and accurate decimeter-grade navigation system that can be used for deployment in the field of autonomous agricultural robots. The novelty of the system is the reliance on a dual GPS configuration based on two u-blox modules that work in conjunction with three low-cost inertial sensors within a Gaussian Sum Filter able to combine multiple Extended Kalman filters dealing with IMU bias and GPS signal loss. The system provides estimation of both position and heading with high precision and robustness, at a significantly lower cost than existing equivalent navigation systems. RoboNav is validated in a commercial vineyard by performing experimental tests using an all-terrain tracked robot commanded to follow a series of GPS waypoints, trying to minimize the crosstrack error and showing average errors on the order of 0.2 m and 0.2∘ for the measurement of position and yaw angle, respectively.

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“…Among them, the LC method is simple implementation but unreliability with less than four visible satellites; 18 while the TC method offers theoretical soundness and high accuracy, which is used in various fields such as airborne navigation, intelligent transportation, and precision agriculture. 19–21…”

Section: Introductionmentioning

confidence: 99%

A novel ambiguity resolution model of BeiDou navigation satellite system/inertial navigation system tightly coupled for kinematic-to-kinematic precise relative positioning

Sun

et al. 2022

International Journal of Distributed Sensor Networks

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The carrier-based kinematic-to-kinematic relative positioning can obtain the precise baseline between two moving stations, which greatly expands the application field of dynamic relative positioning. However, the relative positioning performance is degraded greatly with low fixation rate of ambiguity with low-cost receivers. Especially, in the complex dynamic environment, ambiguity resolution effect is influenced by the satellite signal blocked, multipath outlier, and abnormal state prediction. Aiming at the problems, a novel inertial navigation system–aided robust adaptive filtering ambiguity resolution model is proposed. In addition, a hierarchical filtering strategy is developed to eliminate ambiguity parameters in BeiDou navigation satellite system/inertial navigation system tightly coupled integrated system. Finally, the precise relative position can be calculated with the “best” ambiguity solution. Both experiments with static data and field vehicle test were carried out to evaluate the algorithm efficiency in different data configurations. The results indicate that IRAFAR-TCRP method can effectively suppress the influence of observation outliers and model prediction abnormalities, which improves the success rate of ambiguity resolution, raises the accuracy as well as the continuity of relative positioning. The success rate of ambiguity resolution with single-frequency BeiDou navigation satellite system can reach 90% in the gross error and abnormal disturbance environments and centimeter-level accuracy can be achieved.

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Integrated RTK/INS Navigation for Precision Agriculture

Cited by 13 publications

References 8 publications

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

RoboNav: An Affordable Yet Highly Accurate Navigation System for Autonomous Agricultural Robots

A novel ambiguity resolution model of BeiDou navigation satellite system/inertial navigation system tightly coupled for kinematic-to-kinematic precise relative positioning

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