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A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System
Junxiang Jiang1, Xiaoji Niu2,3, Ruonan Guo4
1GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|August 7, 2019
Summary
This study introduces a new hybrid optimizer for tightly-coupled vision-aided inertial navigation systems. The novel method improves efficiency and achieves competitive results in motion tracking using visual and inertial data.
Area of Science:
- Robotics
- Computer Vision
- Sensor Fusion
Background:
- Visual and inertial measurement fusion is crucial for motion tracking in robotics.
- Existing methods for vision-aided inertial navigation have limitations.
Purpose of the Study:
- To develop a novel hybrid sliding window optimizer for tightly-coupled vision-aided inertial navigation systems.
- To improve the efficiency of information fusion in these systems.
Main Methods:
- A hybrid sliding window optimizer combining conditioning-based and prior-based methods.
- A novel distributed marginalization method based on the multi-state constraints method.
Main Results:
- The proposed algorithm demonstrates competitive performance compared to existing methods.
- Significant efficiency improvements were observed with the new distributed marginalization technique.
Conclusions:
- The novel hybrid optimizer effectively fuses visual and inertial data for enhanced motion tracking.
- The developed methods offer a more efficient and competitive solution for vision-aided inertial navigation.
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