A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots

Flying robots require a combination of accuracy and low latency in their state estimation in order to achieve stable and robust flight. However, due to the power and payload constraints of aerial platforms, state estimation algorithms must provide these qualities under the computational constraints of embedded hardware. Cameras and inertial measurement units (IMUs) satisfy these power and payload constraints, so visualinertial odometry (VIO) algorithms are popular choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is not clear from existing results in the literature, however, which VIO algorithms perform well under the accuracy, latency, and computational constraints of a flying robot with onboard state estimation. This paper evaluates an array of publicly-available VIO pipelines (MSCKF, OKVIS, ROVIO, VINS-Mono, SVO+MSF, and SVO+GTSAM) on different hardware configurations, including several singleboard computer systems that are typically found on flying robots. The evaluation considers the pose estimation accuracy, per-frame processing time, and CPU and memory load while processing the EuRoC datasets, which contain six degree of freedom (6DoF) trajectories typical of flying robots. We present our complete results as a benchmark for the research community. Narrated video presentation: https://youtu.be/ymI3FmwU9AY Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-150440 Published Version Originally published at: Delmerico, Jeffrey; Scaramuzza, Davide (2018). A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots. In: IEEE International Conference on Robotics and Automation (ICRA), 2018., Brisbane, 21 May 2018 25 May 2018, 1-8. A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots Jeffrey Delmerico and Davide Scaramuzza Abstract— Flying robots require a combination of accuracy and low latency in their state estimation in order to achieve stable and robust flight. However, due to the power and payload constraints of aerial platforms, state estimation algorithms must provide these qualities under the computational constraints of embedded hardware. Cameras and inertial measurement units (IMUs) satisfy these power and payload constraints, so visualinertial odometry (VIO) algorithms are popular choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is not clear from existing results in the literature, however, which VIO algorithms perform well under the accuracy, latency, and computational constraints of a flying robot with onboard state estimation. This paper evaluates an array of publicly-available VIO pipelines (MSCKF, OKVIS, ROVIO, VINS-Mono, SVO+MSF, and SVO+GTSAM) on different hardware configurations, including several singleboard computer systems that are typically found on flying robots. The evaluation considers the pose estimation accuracy, per-frame processing time, and CPU and memory load while processing the EuRoC datasets, which contain six degree of freedom (6DoF) trajectories typical of flying robots. We present our complete results as a benchmark for the research community. Flying robots require a combination of accuracy and low latency in their state estimation in order to achieve stable and robust flight. However, due to the power and payload constraints of aerial platforms, state estimation algorithms must provide these qualities under the computational constraints of embedded hardware. Cameras and inertial measurement units (IMUs) satisfy these power and payload constraints, so visualinertial odometry (VIO) algorithms are popular choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is not clear from existing results in the literature, however, which VIO algorithms perform well under the accuracy, latency, and computational constraints of a flying robot with onboard state estimation. This paper evaluates an array of publicly-available VIO pipelines (MSCKF, OKVIS, ROVIO, VINS-Mono, SVO+MSF, and SVO+GTSAM) on different hardware configurations, including several singleboard computer systems that are typically found on flying robots. The evaluation considers the pose estimation accuracy, per-frame processing time, and CPU and memory load while processing the EuRoC datasets, which contain six degree of freedom (6DoF) trajectories typical of flying robots. We present our complete results as a benchmark for the research community.