SLAM with SC-PHD filters: an underwater vehicle application

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Lee, Chee Sing
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Nagappa, Sharad
orcId Palomeras Rovira, Narcís​ researcherId Palomeras Rovira, Narcís​ scopusId Palomeras Rovira, Narcís​ ​
Palomeras Rovira, Narcís
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Clark, Daniel E.
orcId Salvi, Joaquim​ ​ scopusId Salvi, Joaquim​ ​
Salvi, Joaquim
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SLAM-C-PHD.pdf embargoed access ​
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The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position ​
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