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Unified Inverse Depth Parametrization for Monocular SLAM

J. Montiel, J. Civera, Andrew Davison

Conference or Workshop Paper
Robotics: Science and Systems
August, 2006

Recent work has shown that the probabilistic SLAM

approach of explicit uncertainty propagation can succeed in

permitting repeatable 3D real-time localization and mapping

even in the `pure vision' domain of a single agile camera

with no extra sensing. An issue which has caused difficulty in

monocular SLAM however is the initialization of features, since

information from multiple images acquired during motion must

be combined to achieve accurate depth estimates. This has led

algorithms to deviate from the desirable Gaussian uncertainty

representation of the EKF and related probabilistic filters during

special initialization steps.

In this paper we present a new unified parametrization for

point features within monocular SLAM which permits efficient

and accurate representation of uncertainty during undelayed

initialisation and beyond, all within the standard EKF (Extended

Kalman Filter). The key concept is direct parametrization of in-

verse depth, where there is a high degree of linearity. Importantly,

our parametrization can cope with features which are so far

from the camera that they present little parallax during motion,

maintaining sufficient representative uncertainty that these points

retain the opportunity to `come in' from infinity if the camera

makes larger movements. We demonstrate the parametrization

using real image sequences of large-scale indoor and outdoor



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