Although Point Clouds Registration is a very well studied problem, with many different solutions, most of the approaches in the literature aims at aligning two dense point clouds. Instead, we tackle the problem of aligning a dense point cloud with a sparse one: a problem that has to be solved, for example, to merge maps produced by different sensors, such as a vision-based sensor and laser scanner or two different laser-based sensors. The most used approach to point clouds registration, Iterative Closest Point (ICP), is also applicable to this sub-problem. We propose an improvement over the standard ICP data association policy and we called it Probabilistic Data Association. It was derived applying statistical inference techniques on a fully probabilistic model. In our proposal, each point in the source point cloud is associated with a set of points in the target point cloud; each association is then weighted so that the weights form a probability distribution. The result is an algorithm similar to ICP but more robust w.r.t. noise and outliers. While we designed our approach to deal with the problem of dense-sparse registration, it can be successfully applied also to standard point clouds registration.

Agamennoni, G., Fontana, S., Siegwart, R., Sorrenti, D. (2016). Point Clouds Registration with Probabilistic Data Association. In IEEE International Conference on Intelligent Robots and Systems (pp.4092-4098). Institute of Electrical and Electronics Engineers Inc. [10.1109/IROS.2016.7759602].

Point Clouds Registration with Probabilistic Data Association

FONTANA, SIMONE
Primo
;
SORRENTI, DOMENICO GIORGIO
Ultimo
2016

Abstract

Although Point Clouds Registration is a very well studied problem, with many different solutions, most of the approaches in the literature aims at aligning two dense point clouds. Instead, we tackle the problem of aligning a dense point cloud with a sparse one: a problem that has to be solved, for example, to merge maps produced by different sensors, such as a vision-based sensor and laser scanner or two different laser-based sensors. The most used approach to point clouds registration, Iterative Closest Point (ICP), is also applicable to this sub-problem. We propose an improvement over the standard ICP data association policy and we called it Probabilistic Data Association. It was derived applying statistical inference techniques on a fully probabilistic model. In our proposal, each point in the source point cloud is associated with a set of points in the target point cloud; each association is then weighted so that the weights form a probability distribution. The result is an algorithm similar to ICP but more robust w.r.t. noise and outliers. While we designed our approach to deal with the problem of dense-sparse registration, it can be successfully applied also to standard point clouds registration.
Si
paper
Control and Systems Engineering; Software; 1707; Computer Science Applications1707 Computer Vision and Pattern Recognition
English
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS October 9-14
9781509037629
Agamennoni, G., Fontana, S., Siegwart, R., Sorrenti, D. (2016). Point Clouds Registration with Probabilistic Data Association. In IEEE International Conference on Intelligent Robots and Systems (pp.4092-4098). Institute of Electrical and Electronics Engineers Inc. [10.1109/IROS.2016.7759602].
Agamennoni, G; Fontana, S; Siegwart, R; Sorrenti, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/141932
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