Globally Asymptotically Stable Sensor-Based Simultaneous Localization and Mapping

Guerreiro, Bruno J.; Batista, Pedro; Silvestre, Carlos; Oliveira, Paulo

doi:10.1109/tro.2013.2273838

Cited by 37 publications

(38 citation statements)

References 24 publications

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“…Guerreiro et al show that the nonlinear system dynamics in sensor based SLAM can be regarded as a time varying linear system and a Kalman filter can be designed to estimate the system state. 9 Although robocentric SLAM formulation can reduce linearization error to some extent, it is clear that the uncertainty of the location estimates of features that have not been observed for a long period of time can become very large due to the presence of the process noise.…”

Section: Feature Based Slam Problemmentioning

confidence: 99%

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A critique of current developments in simultaneous localization and mapping

Huang

Dissanayake

2016

International Journal of Advanced Robotic Systems

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The number of research publications dealing with the simultaneous localization and mapping problem has grown significantly over the past 15 years. Many fundamental and practical aspects of simultaneous localization and mapping have been addressed, and some efficient algorithms and practical solutions have been demonstrated. The aim of this paper is to provide a critical review of current theoretical understanding of the fundamental properties of the SLAM problem, such as observability, convergence, achievable accuracy and consistency. Recent research outcomes associated with these topics are briefly discussed together with potential future research directions.

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Section: Feature Based Slam Problemmentioning

confidence: 99%

“…8,9 The practical behaviour of EKF SLAM algorithms is significantly influenced by the linearization errors. For example, the theoretical lower bounds could be violated due to the fact that Jacobians with respect to the same landmark are evaluated at different estimate values at different steps.…”

Section: Convergencementioning

confidence: 99%

A critique of current developments in simultaneous localization and mapping

Huang

Dissanayake

2016

International Journal of Advanced Robotic Systems

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“…Furthermore, within the first class of (online) methods, a vast literature exists for addressing SfM: for instance, as a non-exhaustive list, Extended Kalman Filter-based solutions have been proposed in [2]- [5], and other approaches exploiting techniques from (deterministic) nonlinear observation can be found in [6]- [13] and references therein. Finally, in [14] the authors nicely discuss the advantages of a sensor-centered recursive SLAM algorithm sharing the same theoretical setting of what presented in this work.…”

Section: Introductionmentioning

confidence: 73%

Active structure from motion for spherical and cylindrical targets

Spica

Giordano

Chaumette

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Structure estimation from motion (SfM) is a classical and well-studied problem in computer and robot vision, and many solutions have been proposed to treat it as a recursive filtering/estimation task. However, the issue of actively optimizing the transient response of the SfM estimation error has not received a comparable attention. In this paper, we provide an experimental validation of a recently proposed nonlinear active SfM strategy via two concrete applications: 3D structure estimation for a spherical and a cylindrical target. The experimental results fully support the theoretical analysis and clearly show the benefits of the proposed active strategy. Indeed, by suitably acting on the camera motion and estimation gains, it is possible to assign the error transient response and make it equivalent to that of a reference linear second-order system with desired poles.

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“…A possible scenario is the use of autonomous vehicles for inspection of structures such as bridges, power lines, windmills etc., raising the need of high accuracy in position and attitude estimates, as the vehicles will have to work closely to the inspection target. In this case, the electromagnetic interference and the existence of ferromagnetic materials may degrade any magnetometer to the point of becoming unusable [1]. Moreover, global navigation satellite systems (GNSS) may be unreliable or severly degraded.…”

Section: Introductionmentioning

confidence: 99%

Cascade attitude observer for the SLAM filtering problem

Bjørne

Brekke

Johansen

2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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Abstract-This article presents an attitude observer that exploits both bearing and range measurements from landmarks, in addition to reference vectors such as magnetometer and accelerometer. It is a gyro bias observer in cascade with a simplified complementary filter, driven by a gyro measurement, in which the gyro bias is estimated by comparing the bearing dynamics with the gyro measurements. The observer is compared to a full complimentary filter, and it is shown that it is more robust to initial gyro bias estimation error compared to the complimentary filter. The article also reveals how this new observer handles magnetometer failure and can use landmarks as reference vectors.

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Globally Asymptotically Stable Sensor-Based Simultaneous Localization and Mapping

Cited by 37 publications

References 24 publications

A critique of current developments in simultaneous localization and mapping

A critique of current developments in simultaneous localization and mapping

Active structure from motion for spherical and cylindrical targets

Cascade attitude observer for the SLAM filtering problem

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