Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

By Ke Sun, Kartik Mohta, Bernd Pfrommer, Michael Watterson, Sikang Liu, Yash Mulgaonkar, Camillo Jose Taylor, Vijay R. Kumar. IEEE Robotics and Automation Letters, 2017

511 citations recorded by the source corpus.

In recent years, vision-aided inertial odometry for state estimation has matured significantly. However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with microaerial vehicles, in which it is difficult to use high-quality sensors and powerful processors because of constraints on size and weight. In this letter, we present a filter-based stereo visual inertial odometry that uses the multistate constraint Kalman filter. Previous work on the stereo visual inertial odometry has resulted in solutions that are computationally expensive. We demonstrate that our stereo multistate constraint Kalman filter (S-MSCKF) is comparable to state-of-the-art monocular solutions in terms of computational cost, while providing significantly greater robustness. We evaluate our S-MSCKF algorithm and compare it with state-of-the-art methods including OKVIS, ROVIO, and VINS-MONO on both the EuRoC dataset and our own experimental datasets demonstrating fast autonomous flight with a maximum speed of $\text{17.5}$ m/s in indoor and outdoor environments. Our implementation of the S-MSCKF is available at https://github.com/KumarRobotics/msckf_vio.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8f4081e6282442a7596d65ebcb129a30528f718d

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Citations in the corpus

Visual-Inertial Navigation: A Concise Review (2019, methodology): “Mourikis and Roumeliotis [18] developed one of the earliest successful VINS algorithms, known as the multistate constraint Kalman filter (MSCKF), which later was applied to the application of spacecraft descent and landing [21] and fast UAV autonomous flight [43].”
A Comprehensive Survey of Visual SLAM Algorithms (2022, methodology): “The MSCKF is considered one of the fastest ﬁlter-based methods in the literature [64], a consequence of its low computational cost [63], which makes this algorithm suitable for embedded implementations.”
OpenVINS: A Research Platform for Visual-Inertial Estimation (2020, methodology): “While there are several open sourced visual-inertial codebases [2]–[8], they are not developed for extensibility and lack proper documentation and evaluation tools, which, in our experience, are crucial for rapid development and This work was partially supported by the University of Delaware (UD)…”
Swarm of micro flying robots in the wild (2022, methodology): “Furthermore, accurate localization using visual-inertial odometry (5, 6) has matured, which has built up a reliable and efficient perception system with probabilistic mapping (7).”