Robot Localization System in a Hard Outdoor Environment

Conceição, Teresa; Santos, Filipe Neves dos; Costa, Paulo; Moreira, António Paulo

doi:10.1007/978-3-319-70833-1_18

Cited by 4 publications

(9 citation statements)

References 6 publications

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“…Model based temperature compensation for the bias in Inertial Measurement Units (IMUs) has been implemented in systems such as those used by the Pixhawk flight controller; however, empirical studies of the sensor measurement variance are lacking for cases of deployed sensors. Previous investigations have shown that the generalised variance of the system exhibits a correlation between the range of the anchor node and the ranging node; this is corroborated by work completed by [9].…”

Section: Model-based Sensor Measurement Variancesupporting

confidence: 53%

“…The Pozyx is a low cost UWB sensor network which deploys the Time of Flight (TOF) range measurement technique, where a mobile tag receives range measurements from anchors with known locations scattered in an environment [15]. Localisation from the Pozyx is accomplished through a lateration (three anchors) or multi-lateration (>3 anchors) process which is solved using a linear least squares (LLS) implementation [9].…”

Section: Ultra Wide-band Positioningmentioning

confidence: 99%

“…State estimation systems such as the Kalman Filter (KF) and its many variants represent a common implementation of recursive state estimation systems [2,9]. These systems estimate an updated state based upon a kinematic model of the system, measurement inputs and measurement variance.…”

Section: Introductionmentioning

confidence: 99%

“…The main aspect to consider within sensor performance in robotics is the issue of variance. Variance is a critical element of robotics and is generally affected by factors such as unpredictable environments, accumulated variance and inaccurate mathematical system modelling [9]. Therefore, it is common practice to integrate multiple sources of information in an attempt to compliment the errors and uncertainties of each source of information [2,10].…”

Section: Introductionmentioning

confidence: 99%

“…This variable may be a function of environmental factors, a component of the agents state or range to the UWB anchor node. In conventional terms, the sensor measurement variance is defined as a constant either as a whole or for discrete dimensions [9]. This may be termed a generalised variance as it is invariant to changes in the environment and represents the general variance of the sensor.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Pointon

McLoughlin

Matthews

et al. 2019

Drones

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In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version.

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Section: Model-based Sensor Measurement Variancesupporting

confidence: 53%

Section: Ultra Wide-band Positioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Pointon

McLoughlin

Matthews

et al. 2019

Drones

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Tether-Based Localisation System for Underwater Robots

Martin

Tang

2022

2022 27th International Conference on Automation and Computing (ICAC)

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Tank-inspection robotic crawlers often rely on localisation techniques such as dead reckoning to survey walls and surfaces. However, the robot position can drift over time when using these techniques, which therefore require precise calibration. This paper presents a novel tether-based system providing a real-time localisation reference tool to help calibrate and understand prevalent localisation technologies, inside uncluttered tanks. The assessed localisation technologies can subsequently be used in cluttered and complex operational tanks. The developed system relies on a mechanical connection between the tank edge and the robotic crawler and is composed of two distinct units. The first unit is the tether orienting platform clamped onto the tank edge and guiding a tether coming out of a draw-wire position sensor. The second unit is the crawler platform mounted on top of the robotic crawler and holding the tip of the tether, while allowing the robot to move freely. The performance of the system is assessed by determining the accuracy on the 3D position of the robot and the accuracy on its 2D heading, over a 1m x 1m area. The positional accuracy with 90% confidence interval is ±39mm and the heading accuracy with 95% confidence interval is ±4.8°.

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Recent Advancements in Agriculture Robots: Benefits and Challenges

Cheng

et al. 2023

Machines

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In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

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Robot Localization System in a Hard Outdoor Environment

Cited by 4 publications

References 6 publications

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Tether-Based Localisation System for Underwater Robots

Recent Advancements in Agriculture Robots: Benefits and Challenges

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