用岭估计融合视觉与激光数据，提升无人机定位精度与稳定性
Ridge Estimation-Based Vision and Laser Ranging Fusion Localization Method for UAVs
- 引入岭估计缓解多传感器数据的严重共线性问题
- 在远距离等受限条件下，定位误差显著降低
- 适合复杂环境下对定位鲁棒性要求高的无人机应用
利用安装在无人机上的多种传感器跟踪和测量目标，是快速准确定位目标的有效手段。本文提出一种基于岭估计的融合定位方法，结合序列影像丰富的场景信息与激光测距的高精度，提升定位准确性。在观测条件受限（如远距离、小交会角、大倾斜角）时，最小二乘法的设计矩阵列向量存在严重共线性，导致病态问题，造成结果不稳定且鲁棒性差。引入岭估计可有效缓解此类共线性。实验表明，该方法相比仅依赖单一信息的地面定位算法，定位精度更高；尤其在观测受限条件下，鲁棒性显著增强。
原文摘要 · Abstract (English)
Tracking and measuring targets using a variety of sensors mounted on UAVs is an effective means to quickly and accurately locate the target. This paper proposes a fusion localization method based on ridge estimation, combining the advantages of rich scene information from sequential imagery with the high precision of laser ranging to enhance localization accuracy. Under limited conditions such as long distances, small intersection angles, and large inclination angles, the column vectors of the design matrix have serious multicollinearity when using the least squares estimation algorithm. The multicollinearity will lead to ill-conditioned problems, resulting in significant instability and low robustness. Ridge estimation is introduced to mitigate the serious multicollinearity under the condition of limited observation. Experimental results demonstrate that our method achieves higher localization accuracy compared to ground localization algorithms based on single information. Moreover, the introduction of ridge estimation effectively enhances the robustness, particularly under limited observation conditions.
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