Monocular Visual Inertial Navigation for Mobile Robots using Uncertainty based Triangulation
| Autor: | Chan Gook Park, Sejong Heo, Jaehyuck Cha |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Mobile robot 02 engineering and technology Kalman filter 020901 industrial engineering & automation Geography Control and Systems Engineering Feature (computer vision) Inertial measurement unit Sliding window protocol 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Computer vision Artificial intelligence Triangulation business Inertial navigation system |
| Zdroj: | IFAC-PapersOnLine. 50:2217-2222 |
| ISSN: | 2405-8963 |
| Popis: | In this paper, we present a robust multi-state constraint Kalman filter (MSCKF) for visual inertial navigation of mobile robots. We assume the hardware of a mobile robot consists of an inertial measurement unit (IMU) and a monocular camera. The MSCKF is a well-known visual inertial navigation algorithm which performs tightly-coupled fusion between IMU and camera measurements over a sliding window of camera poses, like fixed lag smoother. The conventional MSCKF calculates the residuals as the differences between camera measurements and the re-projected points from the triangulated 3D point, which is calculated by using camera measurements and the pose information over the sliding window. However, the uncertainties of camera poses and image measurements are not considered in this triangulation process. Our algorithm is enforced to estimate robust and precise results by providing a good linearization point related with a feature 3D position based on uncertainty based triangulation, which considers the uncertainties of all sources related with triangulation. The proposed algorithm is validated by the dataset, which is generated known trajectory and features, and real world experimental datasets. |
| Databáze: | OpenAIRE |
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