/Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig

Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig

Michael Kaess and Frank Dellaert
Journal Article, Carnegie Mellon University, Computer Vision and Image Understanding, CVIU, Vol. 114, No. 2, pp. 286-296, February, 2010

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We propose to use a multi-camera rig for simultaneous localization and mapping (SLAM), providing flexibility in sensor placement on mobile robot platforms while exploiting the stronger localization constraints provided by omni-directional sensors. In this context, we present a novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion. Our approach circumvents the combinatorial data association problem by using an incremental expectation maximization algorithm. In the expectation step we deter- mine a distribution over correspondences by sampling. In the maximization step, we find optimal parameters of a density over the robot motion and environment struc- ture. By summarizing the sampling results in so-called virtual measurements, the resulting optimization simplifies to the equivalent optimization problem for known correspondences. We present results for simulated data, as well as for data obtained by a mobile robot equipped with a multi-camera rig.

BibTeX Reference
author = {Michael Kaess and Frank Dellaert},
title = {Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig},
journal = {Computer Vision and Image Understanding, CVIU},
year = {2010},
month = {February},
volume = {114},
number = {2},
pages = {286-296},
keywords = {localization, mapping, mobile robot, multi-camera rig, omni-directional, SFM},