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Dimensionless Monocular SLAM

J. Civera, Andrew Davison, J. Montiel

Conference or Workshop Paper
Iberian Conference of Pattern Recognition and Image Analysis
June, 2007
Abstract

It has recently been demonstrated that the fundamental com-

puter vision problem of structure from motion with a single camera can

be tackled using the sequential, probabilistic methodology of monocu-

lar SLAM (Simultaneous Localisation and Mapping). A key part of this

approach is to use the priors available on camera motion and scene struc-

ture to aid robust real-time tracking and ultimately enable metric motion

and scene reconstruction. In particular, a scene ob ject of known size is

normally used to initialise tracking.

In this paper we show that real-time monocular SLAM can be initialised

with no prior knowledge of scene ob jects within the context of a powerful

new dimensionless understanding and parameterisation of the problem.

When a single camera moves through a scene with no extra sensing, the

scale of the whole motion and map is not observable, but we show that

up-to-scale quantities can be robustly estimated.

Further we describe how the monocular SLAM state vector can be par-

titioned into two parts: a dimensionless part, representing up-to-scale

scene and camera motion geometry, and an extra metric parameter rep-

resenting scale. The dimensionless parameterisation permits tuning of

the probabilistic SLAM filter in terms of image values, without any as-

sumptions about scene scale, but scale information can be put back into

the estimation if it becomes available.

Experimental results with real image sequences showing SLAM without

an initialisation ob ject, different image tuning examples and scenes with

the same underlying dimensionless geometry are presented.

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