Ground texture localization using a downward-facing camera offers a low-cost, high-precision localization solution that is robust to dynamic environments and requires no environmental modification. We present a significantly improved bag-of-words (BoW) image retrieval system for ground texture localization, achieving substantially higher accuracy for global localization and higher precision and recall for loop closure detection in SLAM. Our approach leverages an approximate $k$-means (AKM) vocabulary with soft assignment, and exploits the consistent orientation and constant scale constraints inherent to ground texture localization. Identifying the different needs of global localization vs. loop closure detection for SLAM, we present both high-accuracy and high-speed versions of our algorithm. We test the effect of each of our proposed improvements through an ablation study and demonstrate our method's effectiveness for both global localization and loop closure detection. With numerous ground texture localization systems already using BoW, our method can readily replace other generic BoW systems in their pipeline and immediately improve their results.

翻译：利用下视相机进行地面纹理定位提供了一种低成本、高精度的定位解决方案，该方法对动态环境具有鲁棒性，且无需对环境进行改造。本文提出了一种显著改进的基于词袋模型（BoW）的图像检索系统，用于地面纹理定位，在全局定位方面实现了更高的准确率，同时在SLAM的回环检测中获得了更高的精确率和召回率。我们的方法利用了带有软分配的近似$k$-均值（AKM）词汇表，并利用了地面纹理定位固有的方向一致性和尺度恒定约束。针对SLAM中全局定位与回环检测的不同需求，我们提出了高精度和高速度两种版本的算法。我们通过消融实验测试了所提出的每项改进的效果，并证明了我们的方法在全局定位和回环检测两方面的有效性。鉴于已有众多地面纹理定位系统使用BoW方法，我们的方法可以方便地替换其流程中的其他通用BoW系统，并立即改善其定位结果。