用几何约束提升图像检索精度，实现高鲁棒性地面纹理定位
Improved Bag-of-Words Image Retrieval with Geometric Constraints for Ground Texture Localization
- 引入软分配的近似k-means词袋模型
- 在全局定位和回环检测上显著提升准确率与召回率
- 适合需要高精度定位的SLAM系统部署
利用俯视摄像头进行地面纹理定位，可提供低成本、高精度的定位方案，对动态环境具有鲁棒性且无需修改环境。本文提出一种显著改进的词袋（BoW）图像检索系统，用于地面纹理定位，在全局定位中实现更高准确率，在SLAM回环检测中提升精度与召回率。方法采用近似k-means（AKM）词汇表并结合软分配机制，充分利用地面纹理定位中方向一致、尺度恒定的几何约束。针对全局定位与回环检测的不同需求，我们设计了高精度与高速两个版本算法。通过消融实验验证各项改进效果，并证明该方法在两种任务中的有效性。由于众多现有地面纹理定位系统已使用BoW，本方法可直接替换其流程中的通用BoW模块，立即提升性能。
原文摘要 · Abstract (English)
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.
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