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，该方案对环境动态变化具有鲁棒性，且无需改造环境。我们提出了一种显著改进的Bag-of-Words（BoW）图像检索系统用于地面纹理定位，在SLAM中实现了全局定位精度的大幅提升，以及回环检测的查准率与查全率的显著提高。我们的方法利用了近似$k$-均值（AKM）词汇表配合软分配策略，并充分挖掘了地面纹理定位中固有的方向一致性与尺度恒定约束。针对SLAM中全局定位与回环检测的不同需求，我们分别提出了高精度与高速度两种算法版本。我们通过消融实验验证了各项改进措施的效果，并证明了本方法在全局定位与回环检测任务中的有效性。鉴于众多现有地面纹理定位系统已采用BoW框架，我们的方法可直接替换其流程中的通用BoW系统，并即时提升其性能。