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.