Mei, Christopher (2007) Laser-augmented omnidirectional vision for 3D localisation and mapping. PhD thesis Informatique temps réel, automatique et robotique, CMA- Centre de mathématiques appliquées, ENSMP p.190.

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Abstract

The problem of estimating the motion of a robot and simultaneously building a representation of the

environment (known as SLAM: Simultaneous Localisation And Mapping) is often considered as an essential

topic of research to build fully autonomous systems that do not require any prior knowledge of the environment

to fulfill their tasks.

The evolution of SLAM is closely linked to the sensors used. Sonars with odometry are often presented

as the first sensors having led to convincing results. Since then, 2D laser range finders have often replaced

sonars when possible because of the higher precision and better signal to noise ratio. However 2D lasers alone

limit SLAM to planar motion estimation and do not provide sufficiently rich information to reliably identify

previously explored regions.

These observations have led us to explore throughout this thesis how to combine an omnidirectional camera

with a laser range finder to help solve some of the challenges of SLAM in large-scale complex environments.

The contributions of this thesis concern a method to calibrate central catadioptric cameras (with the development

of an opensource toolbox available on the author’s website) and find the relative position between an

omnidirectional sensor and a laser range finder. How to represent lines and planes for motion estimation is also

studied with the use of Lie algebras to provide a minimal parameterisation. Finally we will detail how laser and

vision can be combined for planar SLAM and 3D structure from motion.

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