Map-based Visual-Inertial Localization: Consistency and Complexity

Zhang, Zhuqing; Jiao, Yanmei; Huang, Shoudong; Wang, Yue; Xiong, Rong

doi:10.48550/arxiv.2204.12173

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…Under the constraints of IMU pre-integration, ORB-SLAM3 optimizes camera poses in a covisibility graph [14]. Although these VIO systems achieve impressive accuracy and robustness, they will suffer from large drift if running in complex environments for a long time, which is insufficient for autonomous vehicle applications [35].…”

Section: Vision-aided or -Based Methodsmentioning

confidence: 99%

RNGC-VIWO: Robust Neural Gyroscope Calibration Aided Visual-Inertial-Wheel Odometry for Autonomous Vehicle

Zhi,

Deng,

Zhang

et al. 2023

Remote Sensing

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Accurate and robust localization using multi-modal sensors is crucial for autonomous driving applications. Although wheel encoder measurements can provide additional velocity information for visual-inertial odometry (VIO), the existing visual-inertial-wheel odometry (VIWO) still cannot avoid long-term drift caused by the low-precision attitude acquired by the gyroscope of a low-cost inertial measurement unit (IMU), especially in visually restricted scenes where the visual information cannot accurately correct for the IMU bias. In this work, leveraging the powerful data processing capability of deep learning, we propose a novel tightly coupled monocular visual-inertial-wheel odometry with neural gyroscope calibration (NGC) to obtain accurate, robust, and long-term localization for autonomous vehicles. First, to cure the drift of the gyroscope, we design a robust neural gyroscope calibration network for low-cost IMU gyroscope measurements (called NGC-Net). Following a carefully deduced mathematical calibration model, NGC-Net leverages the temporal convolutional network to extract different scale features from raw IMU measurements in the past and regress the gyroscope corrections to output the de-noised gyroscope. A series of experiments on public datasets show that our NGC-Net has better performance on gyroscope de-noising than learning methods and competes with state-of-the-art VIO methods. Moreover, based on the more accurate de-noised gyroscope, an effective strategy for combining the advantages of VIWO and NGC-Net outputs is proposed in a tightly coupled framework, which significantly improves the accuracy of the state-of-the-art VIO/VIWO methods. In long-term and large-scale urban environments, our RNGC-VIWO tracking system performs robustly, and experimental results demonstrate the superiority of our method in terms of robustness and accuracy.

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Section: Vision-aided or -Based Methodsmentioning

confidence: 99%