Autonomous Mobile Robots operating in indoor industrial environments require a localization system that is reliable and robust. While Visual Odometry (VO) can offer a reasonable estimation of the robot's state, traditional VO methods encounter challenges when confronted with dynamic objects in the scene. Alternatively, an upward-facing camera can be utilized to track the robot's movement relative to the ceiling, which represents a static and consistent space. We introduce in this paper Ceiling-DSO, a ceiling-vision system based on Direct Sparse Odometry (DSO). Unlike other ceiling-vision systems, Ceiling-DSO takes advantage of the versatile formulation of DSO, avoiding assumptions about observable shapes or landmarks on the ceiling. This approach ensures the method's applicability to various ceiling types. Since no publicly available dataset for ceiling-vision exists, we created a custom dataset in a real-world scenario and employed it to evaluate our approach. By adjusting DSO parameters, we identified the optimal fit for online pose estimation, resulting in acceptable error rates compared to ground truth. We provide in this paper a qualitative and quantitative analysis of the obtained results.

翻译：在室内工业环境中运行的自主移动机器人需要可靠且鲁棒的定位系统。虽然视觉里程计（VO）能够提供机器人状态的合理估计，但传统VO方法在面对场景中的动态物体时会遇到挑战。另一种方法是利用朝上安装的摄像头来追踪机器人相对于天花板的运动，因为天花板代表了一个静态且稳定的空间。本文提出Ceiling-DSO，一种基于直接稀疏里程计（DSO）的天花板视觉系统。与其他天花板视觉系统不同，Ceiling-DSO充分利用了DSO通用化的数学表达形式，避免了对天花板上可观测形状或地标特征的先验假设。这种方法确保了该技术对各种天花板类型的适用性。由于目前缺乏公开的天花板视觉数据集，我们在真实场景中创建了定制数据集，并利用该数据集评估我们的方法。通过调整DSO参数，我们找到了最适合在线位姿估计的配置方案，相较于地面真实数据获得了可接受的误差率。本文对实验结果进行了定性与定量分析。