Research on indoor fusion positioning algorithm based on SLAM/UWB
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摘要: 精确且稳定的自主定位是移动机器人在室内环境下实现自主导航的前提，针对室内定位中视觉即时定位与地图构建(SLAM)存在的累计误差以及环境因素导致超宽带(UWB)定位精度下降的问题，提出一种基于SLAM/UWB的室内融合定位算法. 首先该算法以扩展卡尔曼滤波(EKF)为基础，将UWB的全局定位坐标和视觉SLAM位移增量进行融合，但考虑到测量噪声易受复杂环境影响，引入阈值检测和自适应测量噪声估计器，以抑制异常值和时变测量噪声对滤波器性能的影响，最后使用智能移动小车在不同的室内场地下进行实验. 实验表明：该算法优于单一的UWB或者视觉SLAM定位方式，并且在复杂室内环境下比传统EKF算法拥有更稳定的定位效果.
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关键词:
- 融合定位 /
- 超宽带(UWB) /
- 视觉即时定位与地图构建(SLAM) /
- 扩展卡尔曼滤波(EKF) /
- 测量噪声估计
Abstract: Accurate and stable autonomous positioning is the prerequisite for mobile robots to achieve autonomous navigation in indoor environment. Aiming at the cumulative error of visual simultaneous localization and mapping (SLAM) in indoor positioning and environmental factors that cause ultra wideband (UWB) positioning accuracy to decline, this paper proposes a SLAM/UWB-based indoor fusion positioning algorithm. First of all, the algorithm is based on the extended Kalman filter (EKF), fusing the UWB global positioning coordinates and the visual SLAM displacement increment. Then considering that the measurement noise is easily affected by the complex environment, threshold detection and adaptive measurement noise estimator are introduced to suppress the influence of abnormal values and time-varying measurement noise on the performance of the filter. Finally, an intelligent mobile car is used to conduct experiments in different indoor venues. Experiments show that the algorithm is better than a single UWB or visual SLAM positioning method, and has a more stable positioning effect than the traditional extended Kalman algorithm in a complex indoor environment. -
表 1 单一定位与本文融合定位误差对比
m 路径 误差类型 定位方式 UWB定位 SLAM定位 融合定位 AB 平均值 0.07 0.07 0.06 最大值 0.51 0.22 0.22 BC 平均值 0.08 0.16 0.11 最大值 0.39 0.57 0.29 CD 平均值 0.30 0.95 0.14 最大值 0.79 1.65 0.34 DA 平均值 0.05 1.56 0.10 最大值 0.30 1.65 0.22 整体 平均值 0.16 0.70 0.10 最大值 0.79 1.65 0.34 
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