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：物体定位在机器人、虚拟现实与增强现实、以及仓库货物运输等应用中至关重要。深度学习的最新进展已实现利用单目视觉相机进行定位。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，这两类方法均面临由环境引发的挑战，如运动模糊、光照变化、重复纹理及无特征结构。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，旨在应对这些挑战。RPR方法受限于不同挑战（例如运动模糊）。通过Lucas-Kanade算法计算连续帧间的光流，并利用辅助小型循环卷积网络预测相对位姿。由于全局坐标系与局部坐标系不匹配，融合绝对位姿与相对位姿是一项复杂任务。当前融合绝对与相对位姿的最先进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。本文提出循环融合网络，以最优方式对齐绝对与相对位姿预测，从而提升绝对位姿预测性能。我们评估了八种不同的循环单元，并构建仿真环境以预训练APR与RPR网络，实现更优的泛化训练。此外，我们模拟搭载运输机器人的仓库场景，在复杂大规模室内环境中记录包含多种场景的大型数据库。通过超参数搜索与实验，验证了所提循环融合方法相较于PGO的有效性。