Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope
Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope
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DOI：
10.1109/access.2019.2930201
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Huang, Shi-Sheng
中科院分区：
文献类型：
--
作者：
Quan, Meixiang;Piao, Songhao;Huang, Shi-Sheng
在本文中，我们提出了一种新的紧耦合的概率单目视觉里程同步定位和映射（VOSLAM）算法，使用车轮和MEMS陀螺仪，它可以提供准确的，强大的，和长期的定位地面机器人。首先，我们提出了一种新的里程表预积分理论流形上，它集成了车轮编码器测量和陀螺仪测量的相对运动约束，是独立的线性化点，并仔细解决了不确定性传播和陀螺仪偏差校正。基于预积分里程计测量模型，我们还引入了里程计误差项，并将其紧密集成到可视化优化框架中。然后，为了引导VOSLAM系统，我们提出了一个简单的地图初始化方法。最后，我们提出了一个完整的定位机制，以最大限度地利用这两个传感线索，它提供了不同的运动跟踪策略时：1）两个测量是可用的; 2）视觉测量不可用;和3）车轮编码器的经验滑移，从而确保准确和强大的运动跟踪。通过大量的实验对所提出的算法进行了评估，实验结果证明了所提出的系统的优越性。
In this paper, we present a novel tightly coupled probabilistic monocular visual-odometric simultaneous localization and mapping (VOSLAM) algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust, and long-term localization for ground robots. First, we present a novel odometer preintegration theory on manifold; it integrates the wheel encoder measurements and gyroscope measurements to a relative motion constraint that is independent of the linearization point and carefully addresses the uncertainty propagation and gyroscope bias correction. Based on the preintegrated odometer measurement model, we also introduce the odometer error term and tightly integrate it into the visual optimization framework. Then, in order to bootstrap the VOSLAM system, we propose a simple map initialization method. Finally, we present a complete localization mechanism to maximally exploit both sensing cues, which provides different strategies for motion tracking when: 1) both measurements are available; 2) visual measurements are not available; and 3) wheel encoders experience slippage, thereby ensuring the accurate and robust motion tracking. The proposed algorithm is evaluated by performing extensive experiments, and the experimental results demonstrate the superiority of the proposed system.