Trajectory tracking using environmental magnetic field for outdoor autonomous mobile robots

Rahok, Sam Ann; Shikanai, Yoshihito; Ozaki, Koichi

doi:10.1109/iros.2010.5651998

Cited by 12 publications

(4 citation statements)

References 14 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some commercial products like automated scanners but they are remote operated vehicles and not autonomous (example Jireh Industries. As extended work, the localization system could be improved (another Kalman filter input) with the magnetic field acquired data, as shown in [11].…”

Section: Related Workmentioning

confidence: 99%

Ultra-Wideband Time of Flight Based Localization System and Odometry Fusion for a Scanning 3 DoF Magnetic Field Autonomous Robot

Lima¹,

Costa²

2017

ROBOT 2017: Third Iberian Robotics Conference

View full text Add to dashboard Cite

Abstract. Solving the robot localization problem is one of the most necessary requirements for autonomous robots. Several methodologies can be used to determine its location as accurately as possible. What makes this difficult is the existence of uncertainty in the sensing of the robot. The uncertain information needs to be combined in an optimal way. This paper stresses a Kalman filter to combine information from the odometry and Ultra Wide Band Time of Flight distance modules, which lacks the orientation. The proposed system validated in a real developed platform performs the fusion task which outputs position and orientation of the robot. It is used to localize the robot and make a 3 DoF scanning of magnetic field in a room. Other examples can be pointed out with the same localization techniques in service and industrial autonomous robots.

show abstract

Section: Related Workmentioning

confidence: 99%

Ultra-Wideband Time of Flight Based Localization System and Odometry Fusion for a Scanning 3 DoF Magnetic Field Autonomous Robot

Lima¹,

Costa²

2017

ROBOT 2017: Third Iberian Robotics Conference

View full text Add to dashboard Cite

show abstract

“…Several sensors can be used for robot localization, such as Global Positioning System (GPS) [6,7], 2D and 3D laser rangefinders [8,9], monocular and RGB-D cameras [10,11], wireless signals [12,13], and magnetic sensors [14]. For indoor localization in particular, a notable exception is the use of GPS, as radio waves from satellites are often blocked, making localization impossible [15].…”

Section: Introductionmentioning

confidence: 99%

“…As magnetic changes are more spatially localized than WiFi signal changes, higher localization accuracies are possible. Furthermore, as magnetic information is not affected by nonmagnetic materials, it is reliable even in crowded environments [14]. This makes magnetic fingerprinting more suitable for navigation in crowded environments than any other of the aforementioned sensors.…”

Section: Introductionmentioning

confidence: 99%

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

Takebayashi

Miyagusuku

Ozaki

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth’s geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people in the robot’s surroundings, magnetic-based localization is ideal for service robotics in supermarkets, hotels, etc. A common approach for magnetic-based localization is to first create a magnetic map of the environment where the robot will be deployed. For this, magnetic samples acquired a priori are used. To generate this map, the collected data is interpolated by training a Gaussian Process Regression model. Gaussian processes are nonparametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. These models are flexible and generate mean predictions as well as the confidence of those predictions, making them ideal for their use in probabilistic approaches. However, their computational and memory cost scales poorly when large datasets are used for training, making their use in large-scale environments challenging. The purpose of this study is to: (i) enable magnetic-based localization on large-scale environments by using a sparse representation of Gaussian processes, (ii) test the effect of several kernel functions on robot localization, and (iii) evaluate the accuracy of the approach experimentally on different large-scale environments.

show abstract

“…1 CAR-TIS TypeR ET4D 119. 5 2010 Fig. 7 The generated whole map, snapshots and an example of particle distribution in the simulation 5.…”

mentioning

confidence: 99%

A Robust Localization for Unknown Obstacle Based on the Gridmap Matching

Tomizawa¹,

Muramatsu²,

Hirai³

et al. 2012

JRSJ

View full text Add to dashboard Cite

In this paper, we describe a robust and fast self position estimation technique for mobile robot in an environment where many unknown obstacles exist. The free-space observation model is the basis of the proposed technique. Although the free-space observation achieves robust self-position estimation, it has a complicated likelihood evaluation, and particles continue to spread to a direction where are no landmarks. In this research, we solve these problems with two likelihood evaluations based on the area of free space and occupied space. We evaluated the robustness and verify the validity of the proposed method by a simulation and an experiment in a real environment.

show abstract

Trajectory tracking using environmental magnetic field for outdoor autonomous mobile robots

Cited by 12 publications

References 14 publications

Ultra-Wideband Time of Flight Based Localization System and Odometry Fusion for a Scanning 3 DoF Magnetic Field Autonomous Robot

Ultra-Wideband Time of Flight Based Localization System and Odometry Fusion for a Scanning 3 DoF Magnetic Field Autonomous Robot

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

A Robust Localization for Unknown Obstacle Based on the Gridmap Matching

Contact Info

Product

Resources

About