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Tracking an RGB-D Camera on Mobile Devices Using an Improved Frame-to-Frame Pose Estimation Method

The simple frame-to-frame tracking used for dense visual odometry is computationally efficient, but regarded as rather numerically unstable, easily entailing a rapid accumulation of pose estimation errors. In this paper, we show that a cost-efficient extension of the frame-to-frame tracking can significantly improve the accuracy of estimated camera poses. In particular, we propose to use a multi-level pose error correction scheme in which the camera poses are reestimated only when necessary against a few adaptively selected reference frames. Unlike the recent successful camera tracking methods that mostly rely on the extra computing time and/or memory space for performing global pose optimization and/or keeping accumulated models, the extended frame-to-frame tracking requires to keep only a few recent frames to improve the accuracy. Thus, the resulting visual odometry scheme is lightweight in terms of both time and space complexity, offering a compact implementation on mobile devices, which do not still have sufficient computing power to run such complicated methods.