| 黄超,黄予昕,杨泽彬,张毅.VIG-SLAM：基于自适应多传感器融合的SLAM算法[J].电子测量与仪器学报,2025,39(5):67-74 |
| VIG-SLAM：基于自适应多传感器融合的SLAM算法 |
| VIG-SLAM: Adaptive multi-sensor fusion-based SLAM algorithm |
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| DOI： |
| 中文关键词: SLAM GPS 位姿图优化 多传感器融合 |
| 英文关键词:SLAM GPS pose graph optimization multi-sensor fusion |
| 基金项目: |
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| Author | Institution |
| Huang Chao | Research and Development Center of Information Accessibility Engineering, Chongqing University of
Posts and Telecommunications, Chongqing 400065, China |
| Huang Yuxin | Research and Development Center of Information Accessibility Engineering, Chongqing University of
Posts and Telecommunications, Chongqing 400065, China |
| Yang Zebin | Research and Development Center of Information Accessibility Engineering, Chongqing University of
Posts and Telecommunications, Chongqing 400065, China |
| Zhang Yi | Research and Development Center of Information Accessibility Engineering, Chongqing University of
Posts and Telecommunications, Chongqing 400065, China |
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| 中文摘要: |
| 在缺乏全球定位系统(GPS)信号的环境中，仅依赖视觉惯性里程计的同步定位与建图(SLAM)算法虽能实现局部精确定位，但长距离移动时累积误差显著，导致定位精度下降。同时，尽管GPS能够提供全局位置信息，但在城市峡谷、隧道等复杂环境中，信号容易受到遮挡和干扰，导致定位性能不稳定，限制了其在复杂环境中的应用。为了解决上述问题，提出了VIG-SLAM算法，将视觉/惯导/轮速计紧耦合定位系统(VIW)与GPS数据进行自适应融合。首先，构建了GPS精度因子模型与异常检测机制，以评估并动态选择适合融合的高质量GPS数据。其次，提出了一种改进的自适应时间差补偿策略，解决GPS与VIW系统时间戳不匹配的问题，同时，在时间差补偿中动态调整GPS信号的权重，提升在复杂环境下的定位精度与鲁棒性。最后，构建了包含GPS约束的全局位姿图优化模型，将GPS全局定位信息作为全局约束，与VIW局部定位信息进行互补，实现大场景下的鲁棒定位。在公开数据集上以及真实实验场景中验证了所提方法的有效性，实验结果表明，相比当前主流视觉SLAM算法，提出的的VIG-SLAM算法平均定位精度至少提高15%，具有较强的鲁棒性和精度优势。 |
| 英文摘要: |
| In environments where GPS signals are unavailable, SLAM algorithms relying solely on visual-inertial odometry can achieve local accurate positioning, but they suffer from significant accumulated errors during long-distance movements, leading to decreased positioning accuracy. Although GPS can provide global location information, its performance is often unstable in complex environments such as urban canyons, tunnels, and indoor spaces, where signals are easily blocked or interfered with, limiting its applicability. To address aforementioned issues, the VIG-SLAM algorithm is proposed, which integrates a tightly-coupled visual/inertial/odometer positioning system with GPS data. First, a GPS accuracy factor model and anomaly detection mechanism are developed to evaluate and dynamically select high-quality GPS data suitable for fusion. Second, an improved adaptive time difference compensation strategy is proposed to solve the problem of timestamp mismatch between GPS and VIW systems. At the same time, the weight of GPS signal is dynamically adjusted in time difference compensation to improve positioning accuracy and robustness in complex environments. Finally, a global pose graph optimization model with GPS constraints is constructed, using GPS global positioning information as a global constraint to complement VIW local positioning, achieving robust positioning in large-scale environments. The proposed method’s effectiveness is validated on public datasets and real-world experimental scenarios, with results showing that the average positioning accuracy of VIG-SLAM algorithm improves by at least 15% compared to current mainstreamvisual SLAM algorithms, demonstrating strong robustness and accuracy advantages. |
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