Header

UZH-Logo

Maintenance Infos

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


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.

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. Narrated video presentation: https://youtu.be/ymI3FmwU9AY

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. Narrated video presentation: https://youtu.be/ymI3FmwU9AY

Statistics

Downloads

85 downloads since deposited on 22 Mar 2018
85 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:25 May 2018
Deposited On:22 Mar 2018 11:59
Last Modified:13 Apr 2018 11:43
Publisher:IEEE
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://rpg.ifi.uzh.ch/docs/ICRA18_Delmerico.pdf
Other Identification Number:merlin-id:16269

Download

Download PDF  'A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots'.
Preview
Content: Published Version
Filetype: PDF
Size: 374kB