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Inverse Depth to Depth Conversion for Monocular SLAM

J. Civera, Andrew Davison, J. Montiel

Conference or Workshop Paper
IEEE International Conference on Robotics and Automation
April, 2007
IEEE
Abstract

Recently it has been shown that an inverse depth

parametrization can improve the performance of real-time

monocular EKF SLAM, permitting undelayed initialization of

features at all depths. However, the inverse depth parametriza-

tion requires the storage of 6 parameters in the state vector for

each map point. This implies a noticeable computing overhead

when compared with the standard 3 parameter XYZ Euclidean

encoding of a 3D point, since the computational complexity of

the EKF scales poorly with state vector size.

In this work we propose to restrict the inverse depth

parametrization only to cases where the standard Euclidean

encoding implies a departure from linearity in the measurement

equations. Every new map feature is still initialized using the

6 parameter inverse depth method. However, as the estima-

tion evolves, if according to a linearity index the alternative

XYZ coding can be considered linear, we show that feature

parametrization can be transformed from inverse depth to XYZ

for increased computational efficiency with little reduction in

accuracy.

We present a theoretical development of the necessary

linearity indices, along with simulations to analyze the influence

of the conversion threshold. Experiments performed with with a

30 frames per second real-time system are reported. An analysis

of the increase in the map size that can be successfully managed

is included.

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