WaRoNav: Warehouse Robot Navigation Based on Multi-view Visual-Inertial Fusion
PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT III(2024)
Abstract
An accurate and globally consistent navigation system is crucial for the efficient functioning of warehouse robots. Among various robot navigation techniques, the tightly-coupled visual-inertial fusion stands out as one of the most promising approaches, owing to its complementary sensing and impressive performance in terms of response time and accuracy. However, the current state-of-the-art visual-inertial fusion methods suffer from limitations such as long-term drifts and loss of absolute reference. To address these issues, this paper proposes a novel globally consistent multi-view visual-inertial fusion framework, called WaRoNav, for warehouse robot navigation. Specifically, the proposed method jointly exploits a downward-looking QR-vision sensor and a forward-looking visual-inertial sensor to estimate the robot poses and velocities in realtime. The downward camera provides absolute robot poses with reference to the global workshop frame. Furthermore, the long-term visual-inertial drifts, inertial biases, and velocities are periodically compensated at spatial intervals of QR codes by minimizing visual-inertial residuals with rigid constraints of absolute poses estimated from downward visual measurements. The effectiveness of the proposed method is evaluated on a developed warehouse robot navigation platform. The experimental results show competitive accuracy against state-of-the-art approaches with the maximal position error of 0.05m and maximal attitude error of 2 degrees, irrespective of the trajectory lengths.
MoreTranslated text
Key words
Warehouse Robot,Navigation,Multiple View,Visual-Inertial Fusion
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined