François Pomerleau

Ph.D. (Autonomous Systems Lab, ETH Zurich)
M.A.Sc. (IntRoLab , Université de Sherbrooke)
B.A.Sc. (Computer Engineering, Université de Sherbrooke)

Current affiliation:
Head of Norlab (Northern Robotics Laboratory)
Laval University
Department of Computer Science and Software Engineering
Pavillon Adrien-Pouliot
Quebec, QC
G1V 0A6
Canada

f.pomerleau [at] gmail.com
skype: f.pomerleau
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François Pomerleau

Videos • Code • Data Sets • Publications

Videos

















Code

Registration Library

Libpointmatcher is a modular ICP library, useful for robotics and computer vision. A special attention as been made to ensure its speed for real-time application. Its source code is fully documented based on doxygen to provide an easy API to developers.

See the GitHub website for more info: libpointmatcher.

ROS Mapping Node

One of the main problems with point-cloud registration solutions is that their efficiency often depends on the application. This package provides a convenient way to tune, test and deploy several registration solutions using the same node. It provides a real-time tracker and mapper in 2D and 3D, based on the libpointmachter and libnabo libraries. This allows a complete configuration of the registration chain through YAML files, see the chain configuration page for a list of modules and their parameters. You can think of this package as the front end of a SLAM system, including everything but loop-closure detection and relaxation.

See the ROS website for more info: ethzasl_icp_mapping.

Data Sets

Challenging Data Sets for Point Cloud Registration Algorithms

Challenging Data Sets

This group of datasets was recorded with the aim to test point cloud registration algorithms in specific environments and conditions. Special care is taken regarding the precision of the "ground truth" positions of the scanner, which is in the millimeter range, using a theodolite. Some examples of the recorded environments can be seen bellow.

See the dedicated website for more info: ASL Datasets.

Review

F. Pomerleau, F. Colas and R. Siegwart (2015), "A Review of Point Cloud Registration Algorithms for Mobile Robotics", Foundations and Trends® in Robotics: Vol. 4: No. 1, pp 1-104. Publisher link, Download link.

Geometric primitives

The topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration.



For a full list of publications, please visit my Google Scholar page. You can also follow me on ResearchGate.