A multi-sensor-based mobile robot localization framework

Duan, Ping; Tian, Guohui; Wu, Hao

doi:10.1109/robio.2014.7090403

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…However, the use of such sensors can be very costly. Another solution is to use different sensors of lower accuracy and then apply a sensor fusion technique to the readings of all sensors in order to get a better estimate of the vehicle’s pose (position and orientation) using multiple low cost sensors [1,2,3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Osman

Hussein

Al-Kaff

et al. 2019

Sensors

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Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.

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Section: Introductionmentioning

confidence: 99%

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Osman

Hussein

Al-Kaff

et al. 2019

Sensors

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“…As the field of autonomous driving continues to evolve, mobile robots with high mobility, such as ground mobile robotic vehicles and lunar rovers, play a pivotal role in mission execution [ 1 , 2 ]. These mobile robots are equipped with a variety of sensors, including navigation cameras (Navcams), hazard avoidance cameras (Hazcams), light detection and ranging (LiDAR), time-of-flight depth cameras (TOF), inertial measurement units (IMU), and others, which enable them to gather diverse information about the surroundings.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot

Liu,

Cui,

Fan

et al. 2024

Sensors

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For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground–object–air cooperation mechanism, termed the “ground surface simulation field (GSSF)—mobile robot -photoelectric transmitter station (PTS)”. Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot.

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“…However, such sensors are usually costly and their accuracy degrades in remote areas or in GPS denied environments. An alternative solution is to use different low cost sensors of lower accuracy, and to apply a sensor fusion technique (Khaleghi et al, 2013) in order to get a better pose estimate of the autonomous system (Al-Kaff et al, 2018;Duan et al, 2014;Kelly and Sukhatme, 2011;Lu et al, 2017;Luo and Chang, 2012;Magrin and Todt, 2016;Osman et al, 2019a;Urmson et al, 2008).…”

Section: Introductionmentioning

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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A multi-sensor-based mobile robot localization framework

Cited by 8 publications

References 21 publications

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot

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

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