Improved Bag-of-Words Image Retrieval with Geometric Constraints for Ground Texture Localization

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.

Autonomous sPOMDP Environment Modeling With Partial Model Exploitation

A state space representation of an environment is a classic and yet powerful tool used by many autonomous robotic systems for efficient and often optimal solution planning. However, designing these representations with high performance is laborious and costly, necessitating an effective and versatile tool for autonomous generation of state spaces for autonomous robots. We present a novel state space exploration algorithm by extending the original surprise-based partially-observable Markov Decision Processes (sPOMDP), and demonstrate its effective long-term exploration planning performance in various environments. Through extensive simulation experiments, we show the proposed model significantly increases efficiency and scalability of the original sPOMDP learning techniques with a range of 31-63% gain in training speed while improving robustness in environments with less deterministic transitions. Our results pave the way for extending sPOMDP solutions to a broader set of environments.