Improving the computational efficiency of ROVIO

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Abstract

ROVIO is one of the state-of-the-art mono visual inertial odometry algorithms. It uses an Iterative Extended Kalman Filter (IEKF) to align features and update the vehicle state simultaneously by including the feature locations in the state vector of the IEKF. This algorithm is single core intensive, which allows using the other cores for other algorithms, such as object detection and path optimization. However, the computational cost of the algorithm grows rapidly with the total number of features. Each feature adds three new states (a 2D bearing vector and inverse depth), leading to bigger matrix multiplications which are computationally expensive. The main computational load of ROVIO is the iterative part of the IEKF. In this work, we reduce the average computational cost of ROVIO by 40% on an NVIDIA Jetson TX2, without affecting the accuracy of the algorithm. This computational gain is mainly achieved by utilizing the sparse matrices in ROVIO.
Original languageEnglish
Title of host publication13th international micro air vehicle conference
Place of PublicationDelft, the Netherlands
Pages47-52
Number of pages6
Publication statusPublished - 2022
Event13th International Micro Air Vehicle Conference - Delft, Netherlands
Duration: 12 Sept 202216 Sept 2022
Conference number: 13

Conference

Conference13th International Micro Air Vehicle Conference
Abbreviated titleIMAV2022
Country/TerritoryNetherlands
CityDelft
Period12/09/2216/09/22

Keywords

  • Hybrid MAVs
  • Incremental Nonlinear Dynamic Inversion
  • UAV

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