Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous approaches adopt geometrical information such as key-point features or local textures to carry out indoor relocalization, but they either easily fail in an environment with visually similar scenes or require many database images. Inspired by the fact that humans often remember places by recognizing unique landmarks, we resort to objects, which are more informative than geometry elements. In this work, we propose a simple yet effective object-based indoor relocalization approach, dubbed AirLoc. To overcome the critical challenges of object reidentification and remembering object relationships, we extract object-wise appearance embedding and inter-object geometric relationships. The geometry and appearance features are integrated to generate cumulative scene features. This results in a robust, accurate, and portable indoor relocalization system, which outperforms the state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7% of accuracy. In addition to exhaustive evaluation, we also carry out real-world tests, where AirLoc shows robustness in challenges like severe occlusion, perceptual aliasing, viewpoint shift, and deformation.

翻译：摘要：室内重定位对于机器人自主探索和人类在商场中借助手机导航等任务至关重要。以往的方法采用几何信息（如关键点特征或局部纹理）实现室内重定位，但这类方法在视觉相似场景中易失效，或需要大量数据库图像。受人类通过识别独特地标记忆场所的启发，我们采用更具信息量的物体替代几何元素。本文提出一种简洁有效的基于物体的室内重定位方法——AirLoc。为克服物体重识别与关系记忆的关键挑战，我们提取物体级外观嵌入特征及物体间几何关系，通过融合几何与外观特征生成累积场景特征。由此构建的鲁棒、精准、便携的室内重定位系统，在房间级重定位任务中PR-AUC提升9.5%、准确率提升7%，性能超越现有最优方法。除全面算法评估外，我们亦开展真实场景测试，验证AirLoc在严重遮挡、感知混淆、视角偏移及形变等挑战下的鲁棒性。