Multi-camera visual-inertial odometry: algorithm design and performance evaluation
| Author(s) | Bloecker, Jesse | |
| Date Accessioned | 2020-10-29T15:10:22Z | |
| Date Available | 2020-10-29T15:10:22Z | |
| Publication Date | 2020 | |
| SWORD Update | 2020-09-20T19:03:36Z | |
| Abstract | It is essential for micro aerial vehicles (MAVs) to be able to track their 3D motion in real time using limited onboard sensing and computational resources. To this end, the Multi-State Constraint Kalman Filter (MSCKF), a variant of the Extended Kalman Filter (EKF), is among the most popular approaches to provide real-time motion tracking using camera and IMU sensors and has been proven capable of accurate localization at reduced computational cost. As cameras are becoming ubiquitous, one of the main objectives of this work is to design an efficient multi-camera MSCKF-based visual-inertial odometry (VIO) algorithm that can utilize an arbitrary number of asynchronous cameras. While the standard MSCKF framework is well defined, it leaves available many design choices in implementation that have significant impact on both localization accuracy and computational complexity. These design choices depend strongly on the computational resources available on the platform of interest, which can vary greatly from platforms such as laptop computers to more resource-constrained system-on-chip (SoC) platforms typically found onboard MAVs. We evaluate the performance of our MSCKF-based VIO algorithm on a set of different computing platforms in order to better understand how to optimize the estimator parameters in a resource-aware way. These parameters include the number of stochastic clones to retain in the state vector and the number of visual feature points to track. In particular, in the design of our multi-camera VIO algorithm, the number of independent camera streams to process is also considered as a design parameter. The results of this study provide insight on selecting the parameters that yield the best possible performance for a given computing platform. | en_US |
| Advisor | Huang, Guoquan | |
| Degree | M.S.M.E. | |
| Department | University of Delaware, Department of Mechanical Engineering | |
| DOI | https://doi.org/10.58088/3hgd-s630 | |
| Unique Identifier | 1202255334 | |
| URL | https://udspace.udel.edu/handle/19716/27897 | |
| Language | en | |
| Publisher | University of Delaware | en_US |
| URI | https://login.udel.idm.oclc.org/login?url=https://www.proquest.com/docview/2447314580?accountid=10457 | |
| Keywords | Autonomous navigation | en_US |
| Keywords | Kalman filter | en_US |
| Keywords | Localization and mapping | en_US |
| Keywords | State estimation | en_US |
| Keywords | Unmanned aerial vehicle | en_US |
| Keywords | Visual inertial odometry | en_US |
| Title | Multi-camera visual-inertial odometry: algorithm design and performance evaluation | en_US |
| Type | Thesis | en_US |