Analysis and improvement of the consistency of extended Kalman filter based SLAM

Huang, Guoquan; Mourikis, Anastasios I.; Roumeliotis, Stergios I.

doi:10.1109/robot.2008.4543252

Cited by 173 publications

(190 citation statements)

References 9 publications

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“…Note the similarity of this result with that of (29). Clearly, the physical interpretation of this result is analogous to that of the two-robot case: the global translation and orientation of the state vector are unobservable.…”

Section: Lemma 42 the Rank Of The Observability Matrixm Of The Ideasupporting

confidence: 68%

“…However, to the best of our knowledge, prior to [15], no work has analytically examined the consistency of CL. In contrast, recent research has focused on the consistency of EKF-based simultaneous localization and mapping (SLAM) (see [24][25][26][27][28][29][30][31]), showing that the computed state estimates tend to be inconsistent. Specifically, Julier and Uhlmann [24] first observed that when a stationary robot measures the relative position of a new landmark multiple times, the estimated variance of the robot's orientation becomes smaller.…”

Section: Related Workmentioning

confidence: 99%

“…In our previous work [29][30][31], we conducted a theoretical analysis of the EKF-SLAM inconsistency, and identified as a fundamental cause the mismatch between the dimensions of the observable subspaces of the linearized system, employed by the EKF, and the underlying nonlinear system. Furthermore, we introduced the first estimates Jacobian (FEJ)-EKF and observability-constrained (OC)-EKF, which significantly outperform the standard EKF and the robocentric mapping algorithm [25], in terms of both accuracy and consistency.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the direction corresponding to the rotation is "missing" from the unobservable subspace of the EKF system model (see (29) and (30)). Therefore, the filter gains "nonexistent" information about the robots' global orientation.…”

Section: Two-robot Casementioning

confidence: 99%

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Observability-based consistent EKF estimators for multi-robot cooperative localization

et al. 2010

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In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. We analytically show that the error-state system model employed in the standard EKF-based CL always has an observable subspace of higher dimension than that of the actual nonlinear CL system. This results in unjustified reduction of the EKF covariance estimates in directions of the state space where no information is available, and thus leads to inconsistency. To address this problem, we adopt an observability-based methodology for designing consistent estimators in which the linearization points are selected to ensure a linearized system model with observable subspace of correct dimension. In particular, we propose two novel observabilityconstrained (OC)-EKF estimators that are instances of this paradigm. In the first, termed OC-EKF 1.0, the filter Jacobians are calculated using the prior state estimates as the linearization points. In the second, termed OC-EKF 2.0, the linearization points are selected so as to minimize their expected errors (i.e., the difference between the linearization point and the true state) under the observability constraints. The proposed OC-EKFs have been tested in simulation and experimentally, and have been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.

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Section: Lemma 42 the Rank Of The Observability Matrixm Of The Ideasupporting

confidence: 68%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Two-robot Casementioning

confidence: 99%

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Observability-based consistent EKF estimators for multi-robot cooperative localization

et al. 2010

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“…One approach is to make use of a robocentric representation for the tracked landmarks and thereby significantly reduce the effect of nonlinearities (Castellanos et al (2004); Civera et al (2009)). As an alternative, Huang et al (2008) propose the use of a socalled observability constrained extended Kalman filter, whereby the inconsistencies can be avoided by using special linearization points while evaluating the system Jacobians. A somewhat related problem is the choice of the specific representation of a landmark's location.…”

Section: Related Workmentioning

confidence: 99%

Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback

Bloesch

Burri

Omari

et al. 2017

The International Journal of Robotics Research

404

263

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This paper presents a visual-inertial odometry framework which tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter (IEKF). By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of non-corner shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark's location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it to other state-of-the-art visual-inertial frameworks. The experimental results show that robust localization with high accuracy can be achieved with this filterbased framework.

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Quantitative Assessment of Robot-Generated Maps

Scrapper¹,

Madhavan²,

Lakaemper³

et al. 2009

Performance Evaluation and Benchmarking of Intelligent Systems

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The Performance Metrics for Intelligent Systems workshop is the only one of its kind dedicated to defining measures and methodologies of evaluating performance of intelligent systems. Started in 2000, the PerMIS series focuses on applications of performance measures to practical problems in commercial, industrial, homeland security, and military applications. It has proved to be an excellent forum for discussions and partnerships, dissemination of ideas, and future collaborations between researchers, graduate students, and practitioners from industry, academia, and government agencies.The main theme of the ninth iteration of the workshop, PerMIS'09, seeks to address the question: Does performance measurement accelerate the pace of advancement for intelligent systems? In addition to the main theme, as in previous years, the workshop will focus on applications of performance measures to practical problems in commercial, industrial, homeland security, and military applications. Topic areas include, but are not limited to:• Defining and measuring aspects of a system:The level of autonomy Human-robot interaction General ChairElena Messina NIST Submission InformationProspective authors are requested to submit a draft paper (max. 8 pages) or an extended abstract (1-2 pages) for review. Invited session proposals can also be submitted as draft papers but should contain 1) a session title and a brief statement of purpose, 2) name and affiliation of the organizer(s), and 3) a preliminary list of speakers. All submissions must be written in English, starting with a succinct statement of the problem, the results achieved, their significance, and a comparison with previous work. Papers are

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Analysis and improvement of the consistency of extended Kalman filter based SLAM

Cited by 173 publications

References 9 publications

Observability-based consistent EKF estimators for multi-robot cooperative localization

Observability-based consistent EKF estimators for multi-robot cooperative localization

Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback

Quantitative Assessment of Robot-Generated Maps

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