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 visual-inertial 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 single-board 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.

Reference:
J. Delmerico, D. Scaramuzza
A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots
IEEE International Conference on Robotics and Automation (ICRA), 2018
PDF: http://rpg.ifi.uzh.ch/docs/ICRA18_Del...

Our research page on visual-inertial odometry:
http://rpg.ifi.uzh.ch/research_vo.html

Affiliation:
Robotics and Perception Group,
Dep. of  Neuroinformatics, ETH Zurich & University of Zurich ,
Dep. of Informatics, University of Zurich,
http://rpg.ifi.uzh.ch/