## Robocentric map joining: Improving the consistency of EKF-SLAM (2007)

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Venue: | Robotics and Autonomous Systems |

Citations: | 34 - 3 self |

### BibTeX

@ARTICLE{Castellanos07robocentricmap,

author = {J. A. Castellanos and R. Martínez-cantín and J. D. Tardós and J. Neira},

title = {Robocentric map joining: Improving the consistency of EKF-SLAM},

journal = {Robotics and Autonomous Systems},

year = {2007},

volume = {55},

pages = {21--29}

}

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### Abstract

In this paper we study the Extended Kalman Filter approach to the 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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Citation Context ...lexity of SLAM in large environments [18,14,11,19]. However, only recently, the consistency issues of the EKF-SLAM algorithm have attracted the attention of the research community. Dissanayake et al. =-=[3]-=- proved three important convergence properties of the EKF-SLAM: (1) the determinant of any submatrix of the map covariance matrix decreases monotonically as observations are successively made; (2) in ... |

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Citation Context ...-vector influence the accuracy of linearization. In the last few years, some works have been reported which propose either alternative linearization techniques [6,7] or even non-parametric approaches =-=[8,9]-=- to avoid those difficulties. In this paper we show that linearization errors lead to inconsistent estimates well before the computational problem arises. The main contribution of the paper is the for... |

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Citation Context ...ncorrelated local maps. 12s5.2 The update step Data association is carried out to match the features of the local map M Rl F with those of the local map M Rl−1 E . We use the Joint Compatibility test =-=[13]-=-, which obtains the largest set of pairings which are jointly compatible, a consensus criteria that reduces the possibility of accepting a spurious pairing. Let Fi and Eiji be two matched features, th... |

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Citation Context ...and the covariance of those pdfs conveniently approximates the optimal solution of this estimation problem. Many successful implementations of this approach have been reported in indoor [12], outdoor =-=[14]-=-, underwater [17] and air-borne [16] applications. The time and memory requirements of the basic EKF-SLAM approach result from the cost of maintaining the full covariance matrix, which is O(n 2 ) wher... |

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Citation Context ...the full covariance matrix, which is O(n 2 ) where n is the number of features in the map. Many recent efforts have concentrated on reducing the computational complexity of SLAM in large environments =-=[18,14,11,19]-=-. However, only recently, the consistency issues of the EKF-SLAM algorithm have attracted the attention of the research community. Dissanayake et al. [3] proved three important convergence properties ... |

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Citation Context ...the full covariance matrix, which is O(n 2 ) where n is the number of features in the map. Many recent efforts have concentrated on reducing the computational complexity of SLAM in large environments =-=[18,14,11,19]-=-. However, only recently, the consistency issues of the EKF-SLAM algorithm have attracted the attention of the research community. Dissanayake et al. [3] proved three important convergence properties ... |

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Citation Context ...wn environment and, concurrently, learning useful information from the surroundings taking into account sensor errors. The most popular approach to SLAM dates back to the seminal work of Smith et al. =-=[1]-=- where the idea of representing the structure of the navigation area in a discrete-time state-space framework was originally presented. They introduced the concept of stochastic map and they developed... |

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Citation Context ... of uncertainty in the es2stimated state-vector influence the accuracy of linearization. In the last few years, some works have been reported which propose either alternative linearization techniques =-=[6,7]-=- or even non-parametric approaches [8,9] to avoid those difficulties. In this paper we show that linearization errors lead to inconsistent estimates well before the computational problem arises. The m... |

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Citation Context ...t 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 et al. =-=[4]-=- and confirmed experimentally by Castellanos et al. [5]. The classical EKF-SLAM linearizes both the motion and sensor models by using a first-order Taylor series expansion around the best available es... |

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Citation Context ...-vector influence the accuracy of linearization. In the last few years, some works have been reported which propose either alternative linearization techniques [6,7] or even non-parametric approaches =-=[8,9]-=- to avoid those difficulties. In this paper we show that linearization errors lead to inconsistent estimates well before the computational problem arises. The main contribution of the paper is the for... |

31 |
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Citation Context ...veniently approximates the optimal solution of this estimation problem. Many successful implementations of this approach have been reported in indoor [12], outdoor [14], underwater [17] and air-borne =-=[16]-=- applications. The time and memory requirements of the basic EKF-SLAM approach result from the cost of maintaining the full covariance matrix, which is O(n 2 ) where n is the number of features in the... |

16 |
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Citation Context ...te-space framework was originally presented. They introduced the concept of stochastic map and they developed a rigorous solution to the SLAM problem using an Extended Kalman Filter (EKF) perspective =-=[2]-=-. The EKF-SLAM approach is characterized by the existence of a discretetime augmented state vector, composed of the location of the vehicle and the location of the map elements, recursively estimated ... |

15 | Unscented SLAM for largescale outdoor environments
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Citation Context ... of uncertainty in the es2stimated state-vector influence the accuracy of linearization. In the last few years, some works have been reported which propose either alternative linearization techniques =-=[6,7]-=- or even non-parametric approaches [8,9] to avoid those difficulties. In this paper we show that linearization errors lead to inconsistent estimates well before the computational problem arises. The m... |

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12 |
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(Show Context)
Citation Context ...at the computed covariances will match the actual estimation errors, which is the true SLAM consistency issue first pointed out by Julier et al. [4] and confirmed experimentally by Castellanos et al. =-=[5]-=-. The classical EKF-SLAM linearizes both the motion and sensor models by using a first-order Taylor series expansion around the best available estimated state-vector, therefore, both the bias and the ... |

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2 |
Stochastic Mapping Using Forward
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- 2001
(Show Context)
Citation Context ...e of those pdfs conveniently approximates the optimal solution of this estimation problem. Many successful implementations of this approach have been reported in indoor [12], outdoor [14], underwater =-=[17]-=- and air-borne [16] applications. The time and memory requirements of the basic EKF-SLAM approach result from the cost of maintaining the full covariance matrix, which is O(n 2 ) where n is the number... |