Precise localization of the mobile wheeled robot using sensor fusion of odometry, visual artificial landmarks and inertial sensors

Nemec, Dušan; Šimák, Vojtech; Janota, Aleš; Hruboš, Marián; Bubeníková, Emília

doi:10.1016/j.robot.2018.11.019

Cited by 27 publications

(11 citation statements)

References 7 publications

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“…The design of the current odometer has mostly been applied to wheeled robots, whose mileage data are obtained by measuring the rotation of the wheel using a built-in encoder to achieve accurate positioning information. However, the wheel odometer has been shown as idle on smooth ground and cannot measure wheel "skidding", which adversely affects the positioning accuracy [9]. However, as opposed to wheeled robots, legged robots are driven by motorized leg joints, which cannot provide accurate mileage data when relying solely on its own structure.…”

Section: Introductionmentioning

confidence: 99%

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

et al. 2022

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Quadruped robots, an important class of unmanned aerial vehicles, have broad potential for applications in education, service, industry, military, and other fields. Their independent positioning plays a key role for completing assigned tasks in a complex environment. However, positioning based on global navigation satellite systems (GNSS) may result in GNSS jamming and quadruped robots not operating properly in environments sheltered by buildings. In this paper, a tightly coupled LiDAR vision inertial odometer (LVIO) is proposed to address the positioning inaccuracy of quadruped robots, which have poor mileage information obtained though legs and feet structures only. With this optimization method, the point cloud data obtained by 3D LiDAR, the image feature information obtained by binocular vision, and the IMU inertial data are combined to improve the precise indoor and outdoor positioning of a quadruped robot. This method reduces the errors caused by the uniform motion model in laser odometer as well as the image blur caused by rapid movements of the robot, which can lead to error-matching in a dynamic scene; at the same time, it alleviates the impact of drift on inertial measurements. Finally, the quadruped robot in the laboratory is used to build a physical platform for verification. The experimental results show that the designed LVIO effectively realizes the positioning of four groups of robots with high precision and strong robustness, both indoors or outdoors, which verify the feasibility and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

et al. 2022

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“…To effectively perform these complex open-field tasks, accurate and precise localization of these agricultural robots, including XYZ position, heading, and attitude (roll and pitch tilts), are essential. Several principles of localization sensors for agricultural robots are real-time kinematic global navigation satellite systems (RTK GNSS) [1]- [5], landmarks detection [6], light laser detection and ranging (LiDAR) [7]- [8], radar [9], sensor fusion with camera [10]- [13], ultrasonic [14], multi-sensor fusion [15]- [16], beacons based [17], localization and mapping (SLAM) [18], laser range finder (LRF) [19], and inertial measurement unit (IMU) [20]. Particularly, RTK GNSS coupled with inertial navigation systems (INS) is extensively employed by farmers due to its relatively higher accuracy and precision compared to other sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

2021

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A novel, low-cost approach to high-accuracy robot localization for agricultural applications using an image-processing triple-laser-guided (TLG) system coupled with an inertial measurement unit (IMU) is presented in this paper. The TLG system consists of a laser-pointing unit (LPU) at the base station and a laser-target unit (LTU) at the mobile robot. The robot's XYZ position and heading are determined from the positions and the angles relative to the field of both LPU and LTU. The robot's roll and pitch are determined by the IMU sensor fusion with complementary filter. The IMU-coupled TLG system is demonstrated on an outdoor, 20 x 21 m flat field at various light intensities. The overall lateral and vertical accuracies of the IMU-coupled TLG system are 1.68 cm and 0.59 cm, respectively. The overall heading, roll, and pitch accuracies of the IMU-coupled TLG system are 0.90º, 0.78º, and 0.76º, respectively. The lateral and heading accuracies of the IMU-coupled TLG system are found to be comparable to commercially available GNSS-INS systems from NovAtel and Trimble, while the total cost of the IMUcoupled TLG system is only a fraction of the total cost of the commercially available localization systems.

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“…For example, using a GPS along with visual and laser-based sensors is one of the traditional sensor fusion cases to locate an outdoor vehicle. 6 Some nonlinear approaches have been proposed to attain proper sensor fusion and data reconstruction. 7 Kolanowski et al.…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion approach for shape estimation of a base-excited cantilever beam

Mohammadi

Nayyeri

Zakerzadeh

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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In smart structures, achieving a reliable set of measurement signals to monitor the system’s performance is critical. Also, the essence of using the optimum batch of sensors and an efficient algorithm to process these signals is significant for active vibration control of these structures. This paper primarily introduces a method of sensor fusion using the Kalman filter as an observer to gain the proper position signal from both an accelerometer and an ultrasonic sensor mounted on the tip of a cantilever beam. The main goal of this procedure is to eliminate both sensors’ shortcomings. Also, we present a novel approach to estimate the overall shape of the beam, using only the tip position signal. To this end, a high-speed camera is used to capture the motion of three markers on the beam under different excitation frequencies. Then, three long short-term memory networks are trained by deep learning methods, using a finite sequence of beam tip position, to act as observers for estimating the shape of the beam. The proposed methods are simulated and then validated by experiments.

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Precise localization of the mobile wheeled robot using sensor fusion of odometry, visual artificial landmarks and inertial sensors

Cited by 27 publications

References 7 publications

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

Sensor fusion approach for shape estimation of a base-excited cantilever beam

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