Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Liu, Fangxu; Li, Xueyuan; Yuan, Shuai; Lan, Wei

doi:10.1109/access.2020.2977889

Cited by 26 publications

(19 citation statements)

References 42 publications

(47 reference statements)

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“…In Liu et al (2020), a multi-innovation UKF is used to fuse the measurement from different sensors in order to estimate the pose and velocity of skid-steer mobile robots. Therein, a dual antenna GNSS, two encoders, and an IMU are used to localize the robot while estimating the slip error components.…”

Section: Related Workmentioning

confidence: 99%

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Osman

Mehrez

Daoud

et al. 2021

Transactions of the Institute of Measurement and Control

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In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

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Section: Related Workmentioning

confidence: 99%

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Osman

Mehrez

Daoud

et al. 2021

Transactions of the Institute of Measurement and Control

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show abstract

“…Authors in [86] use an improved version of kalman filter called the error state kalman filter which uses measurements from RTK GPS, lidar and IMU for robust state estimation. Liu et al [87] present a Multi-Innovation UKF (MI-UKF), which utilizes a history of innovations in the update stage to improve the accuracy of the state estimate, it fuses IMU, encoder and GPS data and estimates the slip error components of the robot.…”

Section: B Localization and Scene Modelingmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Cimarelli³

et al. 2021

Preprint

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The knowledge that an intelligent and autonomous mobile robot has and is able to acquire of itself and the environment, namely the situation, limits its reasoning, decision-making, and execution skills to efficiently and safely perform complex missions. Situational awareness is a basic capability of humans that has been deeply studied in fields like Psychology, Military, Aerospace, Education, etc., but it has barely been considered in robotics, which has focused on ideas such as sensing, perception, sensor fusion, state estimation, localization and mapping, spatial AI, etc. In our research, we connected the broad multidisciplinary existing knowledge on situational awareness with its counterpart in mobile robotics. In this paper, we survey the state-of-the-art robotics algorithms, we analyze the situational awareness aspects that have been covered by them, and we discuss their missing points. We found out that the existing robotics algorithms are still missing manifold important aspects of situational awareness. As a consequence, we conclude that these missing features are limiting the performance of robotic situational awareness, and further research is needed to overcome this challenge. We see this as an opportunity, and provide our vision for future research on robotic situational awareness.

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“…Chen [30] used a UKF-based adaptive variable structural observer with dynamic correction (AUKF) for vehicle body sideslip angle. Liu, F. [31] developed a multi-innovative unscented Kalman filter (MI-UKF) based on an ICR kinetic model of a skid-steered robot. As compared with EKF based on Taylor expansion to realize linear approximation, the UKF performed more accurately in high nonlinear work conditions [32].…”

Section: Vehicle Motion States Estimationmentioning

confidence: 99%

Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

et al. 2021

Self Cite

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Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified.

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Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Cited by 26 publications

References 42 publications

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

From SLAM to Situational Awareness: Challenges and Survey

Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment

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