Abstract-In order to develop Vision-aided Inertial Navigation Systems (VINS) on mobile devices, such as cell phones and tablets, one needs to consider two important issues, both due to the commercial-grade underlying hardware: (i) The unknown and varying time offset between the camera and IMU clocks (ii) The rolling-shutter effect caused by CMOS sensors. Without appropriately modelling their effect and compensating for them online, the navigation accuracy will significantly degrade. In this work, we introduce a linear-complexity algorithm for fusing inertial measurements with time-misaligned, rolling-shutter images using a highly efficient and precise linear interpolation model. As a result, our algorithm achieves a better accuracy and improved speed compared to existing methods. Finally, we validate the superiority of the proposed algorithm through simulations and real-time, online experiments on a cell phone.
The objective of this paper is to provide consistent, real-time 3D localization capabilities to mobile devices navigating within previously mapped areas. To this end, we introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which explicitly considers the uncertainty of the prior map, by employing the sparse Cholesky factor of the map's Hessian, instead of its dense covariance-as is the case for the Schmidt-Kalman filter (SKF). By doing so, the C-SKF has memory requirements typically linear in the size of the map, as opposed to quadratic for storing the map's covariance. Moreover, and in order to bound the processing needs of the C-SKF (between linear and quadratic in the size of the map), we introduce a relaxation of the C-SKF algorithm, the sC-SKF, which operates on the Cholesky factors of independent submaps resulting from dividing the trajectory and observations used for constructing the map into overlapping segments. Lastly, we assess the processing and memory requirements of the proposed C-SKF and sC-SKF algorithms, and compare their positioning accuracy against other approximate map-based localization approaches that employ measurement-noise-covariance inflation to compensate for the map's uncertainty.• We introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which employs the Hessian's Cholesky factor to compactly represent the map's uncertainty, and efficiently compute consistent map-based updates.• We introduce the sub-map (s)C-SKF, a relaxation of the C-SKF, which employs multiple, independent sub-maps
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