The 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010

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On the accurate covariance estimation of the MSISpIC algorithm

Angelos Mallios
Universitat de Girona
Spain

Pere Ridao
Universitat de Girona
Spain

Fransesco Maurelli
Heriot-Watt University
United Kingdom

Yvan Petillot
Heriot-Watt University
United Kingdom

Abstract:
A number of existing methods for estimating the displacement of a robotic vehicle incorporates scan matching algorithms. There are many different scan matching algorithms but only few of them base their estimation on a probabilistic framework. To correctly integrate the scan matching estimate in a SLAM algorithm under Gaussian assumption, it is needed to know not only the displacement estimation but also its covariance. Recent methods of estimating the covariance are based on the analysis of the error function being minimized and are independent of the minimization algorithm.
In this paper, we studding those methods and properly apply them in a closed-form to MSISpIC, a probabilistic scan matching algorithm based on pIC, with range scans gathered from mechanical scanning imaging sonar. The MSISpIC is the base algorithm used in a previous work that the authors had proposed, a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. This approach is verified with extensive simulations and numerical calculations.

 

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