Design of the navigation system through the fusion of IMU and wheeled encoders

Yu, Shibing; Jiang, Zhengwu

doi:10.1016/j.comcom.2020.07.009

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Heading estimation is also a critical issue, especially during steering maneuvers in narrow paths, due to gyroscope inaccuracies that lead to typical heading drift [9] and poor steering angle prediction [10]. Previous research on autonomous navigation has proposed the integration of different sensor modalities, including an Inertial Measurement Unit (IMU) and GPS [11,12], IMU and wheel encoders [13,14], and LiDAR-only applications [15,16]. Combination of an IMU and camera within an Extended Kalman Filter to estimate the Euler angles with good accuracy was discussed in [17,18].…”

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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