Limits to the consistency of EKF-based SLAM

Castellanos, José A.; Neira, José; Tardós, Juan D.

doi:10.1016/s1474-6670(17)32063-3

Cited by 182 publications

(209 citation statements)

References 8 publications

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“…2(b) compares the vehicle heading uncertainty, using a robot centered representation, of the ideal errorfree EKF against the robocentric mapping approach reported in [5] and the new robocentric map joining algorithm. As observed from the figure, both algorithms obtain a non-optimistic estimation for the vehicle heading uncertainty along the vehicle trajectory, which makes loop closing detection possible.…”

Section: Simulationmentioning

confidence: 99%

“…However, it is important to note that these theoretical results only refer to the evolution of the covariance matrices computed by the EKF in the ideal linear case. They overlook the fact that, given that SLAM is a nonlinear problem, there is no guarantee that the computed covariances will match the actual estimation errors, which is the true SLAM consistency issue first pointed out by Julier and Uhlmann [4] and confirmed experimentally by Castellanos et al [5].…”

Section: Introductionmentioning

confidence: 99%

“…After a certain time, the vehicle and the features currently observed have a growing absolute uncertainty that propagates to the measurement equations introducing errors. Here we build each local map using a robot centered representation [5], i.e. relative to a reference frame R attached to the vehicle.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robocentric map joining: Improving the consistency of EKF-SLAM

Castellanos

Martínez-Cantín

Tardós

et al. 2007

Robotics and Autonomous Systems

190

141

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In this paper 1 we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKF-SLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency, specially in those situations where uncertainty surpasses a certain threshold. We propose a mapping algorithm, Robocentric Map Joining, which improves consistency of the EKF-SLAM algorithm by limiting the level of uncertainty in the continuous evolution of the stochastic map: (1) by building a sequence of independent local maps, and (2) by using a robot centered representation of each local map. Simulations and a large-scale indoor/outdoor experiment validate the proposed approach.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robocentric map joining: Improving the consistency of EKF-SLAM

Castellanos

Martínez-Cantín

Tardós

et al. 2007

Robotics and Autonomous Systems

190

141

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“…In this work we consider conditionally independent local maps (Piniés & Tardós, 2007) because they allow sharing vital information between consecutive maps (in this case the underwater vehicle state). Strong empirical evidence also suggests that the use of local maps also improves the consistency of EKF-based SLAM algorithms (Castellanos, Neira, & Tardós, 2004;Huang & Dissanayake, 2007).…”

Section: Introductionmentioning

confidence: 99%

Underwater SLAM in man‐made structured environments

Ribas

Ridao

Tardós

et al. 2008

Journal of Field Robotics

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167

115

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This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas, and marine platforms. A mechanically scanned imaging sonar is used to obtain information about the location of vertical planar structures present in such environments. A robust voting algorithm has been developed to extract line features, together with their uncertainty, from the continuous sonar data flow. The obtained information is incorporated into a feature-based simultaneous localization and mapping (SLAM) algorithm running an extended Kalman filter. Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Moreover, a procedure to build and maintain a sequence of local maps and to posteriorly recover the full global map has been adapted for the application presented. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach. C 2008 Wiley Periodicals, Inc.

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“…It is well known that the EKF linearization can lead to filter divergence [5], [6], [7]. In the last few years, several works have proposed techniques to reduce the linearization effect.…”

mentioning

confidence: 99%

Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM

Martínez-Cantín

Freitas

Castellanos

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a new particle method, with stochastic parameter estimation, to solve the SLAM problem. The underlying algorithm is rooted on a solid probabilistic foundation and is guaranteed to converge asymptotically, unlike many existing popular approaches. Moreover, it is efficient in storage and computation. The new algorithm carries out filtering only in the marginal filtering space, thereby allowing for the recursive computation of low variance estimates of the map. The paper provides mathematical arguments and empirical evidence to substantiate the fact that the new method represents an improvement over the existing particle filtering approaches for SLAM, which work on the joint path state space.

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Limits to the consistency of EKF-based SLAM

Cited by 182 publications

References 8 publications

Robocentric map joining: Improving the consistency of EKF-SLAM

Robocentric map joining: Improving the consistency of EKF-SLAM

Underwater SLAM in man‐made structured environments

Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM

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