It was a nice experience to go through the work of Jose-Luis Blanco et al. titled "An Alternative to the Mahalanobis distance for determining optimal correspondences in data association".
They propose a new distance metric that can replace the state-of-the-art Mahalanobis distance to determine optimal correspondences. They have specifically proposed this for the data association problem in SLAM (Simultaneous Localization and Mapping). They clearly show how this new metric called as Matching Likelihood (ML) is a more generalized metric and the Squared Mahalanobis Distance (SMD) turns out to be a part of it.
Theoretically and experimentally they validate that ML always gives better correspondences than SMD.
The paper can be downloaded from this link
http://ingmec.ual.es/~jlblanco/papers/blanco2012amd.pdf
They propose a new distance metric that can replace the state-of-the-art Mahalanobis distance to determine optimal correspondences. They have specifically proposed this for the data association problem in SLAM (Simultaneous Localization and Mapping). They clearly show how this new metric called as Matching Likelihood (ML) is a more generalized metric and the Squared Mahalanobis Distance (SMD) turns out to be a part of it.
Theoretically and experimentally they validate that ML always gives better correspondences than SMD.
The paper can be downloaded from this link
http://ingmec.ual.es/~jlblanco/papers/blanco2012amd.pdf
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