iLoc: An Adaptive, Efficient, and Robust Visual Localization System

Peng Yin, Shiqi Zhao, Jing Wang, Ruohai Ge, Jianmin Ji, Yeping Hu, Huaping Liu, Jianda Han

Abstract: In this article, we introduce iLoc, an innovative visual localization system designed to enhance the autonomy and adaptability of robotic agents in long-term and large-scale applications. iLoc specializes in: 1) extracting stable and consistent descriptors for place recognition, unaffected by changes in viewpoint and illumination; 2) performing swift and precise global relocalization to establish a robot's position within a large and complex environment; and 3) generating real-time tracking trajectories aligned with reference maps, ensuring continual orientation within known spaces. Distinctively, iLoc incorporates a transformer-based learning module and an attention-enhanced recognition approach, enabling it to adapt to diverse environmental and viewpoint conditions. iLoc leverages a coarse-to-fine global feature matching technique for enhanced localization and integrates robust state estimation combining visual odometry and loop closures through local refinement and pose graph optimization. iLoc demonstrates remarkable proficiency in place recognition, achieving localization over distances of up to 2 km within 0.5 s with average accuracy at 1 m. It maintains stable localization accuracy, even under variable conditions. Its versatile design allows integration across various environments, significantly broadening the scope of universal localization capabilities in robotics. iLoc represents a substantial step forward in visual-based localization systems, delivering unparalleled speed and accuracy in place recognition. Its ability to adapt and respond to diverse environmental stimuli marks it as a crucial tool in advancing the field of robotic localization.

External IDs:dblp:journals/trob/YinZWGJHLH25