Stereo Visual Inertial Odometry for Robots with Limited Computational Resources*

Current existing stereo visual odometry algorithms are computationally too expensive for robots with restricted resources. Executing these algorithms on such robots leads to a low frame rate and unacceptable decay in accuracy. We modify S-MSCKF, one of the most computationally efficient stereo Visual Inertial Odometry (VIO) algorithm, to improve its speed and accuracy when tracking low numbers of features. Specifically, we implement the Inverse Lucas-Kanade (ILK) algorithm for feature tracking and stereo matching. An outlier detector based on the average sum square difference of the template and matching warp in the ILK ensures higher robustness, e.g., in the presence of brightness changes. We restrict stereo matching to slide the window only in the x-direction to further decrease the computational costs. Moreover, we limit detection of new features to the regions of interest that have too few features. The modified S-MSCKF uses half of the processing time while obtaining competitive accuracy. This allows the algorithm to run in real-time on the extremely limited Raspberry Pi Zero single-board computer.This work was supported by Royal Brinkman Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio

A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors

We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject outliers. We compare our algorithm to a state-of-the-art Extended Kalman Filter and show its advantages when dealing with heavy-tailed noise, which is frequently encountered in Ultra-Wideband ranging. Furthermore, we analyze the algorithm's performance when reducing the number of beacons for measurements and we implement the code on a 30 g Crazyflie drone, to show its ability to run on computationally limited devices.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio