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Abstract-We propose a method of monocular camerainertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operate using different representations. The separation of representations allows us not only to move the computational load of full map inference to a ground station, but to further reduce the computational cost of on-board tracking for pose estimation. Our primary contribution is to show how the cost of tracking the vehicle pose on-board can be substantially reduced by estimating the camera motion directly in the image frame, rather than in the world co-ordinate frame. We demonstrate our method on an Ascending Technologies Pelican quad-rotor, and show that we can track the vehicle pose with reduced on-board computation but without compromised navigation accuracy.

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Computational complexity of the Kalman filter grows at least quadratically with the number of dimensions in the filter. This is a particular problem for applications like monocular simultaneous localization and mapping (SLAM) where it is not possible to run a single filter on a large map with many thousands of landmarks.This paper presents a method for dramatically reducing the computational complexity of the Kalman filters by reducing the dimensionality as information is acquired. We prove the validity of our method by applying it to monocular SLAM, where there is a large number of dimensions in the filter that are not subject to process noise (the landmark locations). This has the effect of reducing the cost of running a filter or allowing a single filter to process a much larger set of landmarks.Our approach also has a role to play within modern efficient sparse matrix approaches to SLAM where local information is coalesced into keyframes using Kalman filters. It also has general applicability to filtered measurement of static quantities where there are large numbers of dimensions that are not subject to process noise.