The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pose from a point cloud, absolute pose regression (APR) methods learn a semantic understanding of the environment through neural networks. However, both fields face challenges caused by the environment such as motion blur, lighting changes, repetitive patterns, and feature-less structures. This study aims to address these challenges by incorporating additional information and regularizing the absolute pose using relative pose regression (RPR) methods. RPR methods suffer under different challenges, i.e., motion blur. The optical flow between consecutive images is computed using the Lucas-Kanade algorithm, and the relative pose is predicted using an auxiliary small recurrent convolutional network. The fusion of absolute and relative poses is a complex task due to the mismatch between the global and local coordinate systems. State-of-the-art methods fusing absolute and relative poses use pose graph optimization (PGO) to regularize the absolute pose predictions using relative poses. In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction. We evaluate eight different recurrent units and construct a simulation environment to pre-train the APR and RPR networks for better generalized training. Additionally, we record a large database of different scenarios in a challenging large-scale indoor environment that mimics a warehouse with transportation robots. We conduct hyperparameter searches and experiments to show the effectiveness of our recurrent fusion method compared to PGO.

翻译：物体定位是机器人、虚拟现实与增强现实以及仓库货物运输等应用中的关键任务。近期深度学习的进展使单目视觉相机定位成为可能。运动恢复结构（SfM）通过点云预测绝对位姿，而绝对位姿回归（APR）方法则通过神经网络学习环境的语义理解。然而，这两类方法均面临运动模糊、光照变化、重复纹理及无特征结构等环境挑战。本研究通过引入额外信息并利用相对位姿回归（RPR）方法对绝对位姿进行正则化应对这些挑战。RPR方法易受运动模糊等不同因素影响。本文采用Lucas-Kanade算法计算连续图像间的光流，并通过辅助小型递归卷积网络预测相对位姿。由于全局与局部坐标系的不匹配，绝对位姿与相对位姿的融合是复杂任务。现有融合绝对与相对位姿的方法采用位姿图优化（PGO）利用相对位姿对绝对位姿预测进行正则化。本文提出递归融合网络以实现绝对与相对位姿预测的最优对齐，从而提升绝对位姿预测精度。我们评估了八种不同递归单元，并构建仿真环境对APR与RPR网络进行预训练以获得更优的泛化能力。此外，我们在模拟仓库运输机器人的大规模室内挑战环境中录制了包含不同场景的大型数据库。通过超参数搜索与实验，证明了相较于PGO方法，本文提出的递归融合方法的有效性。