用天花板视觉实现工业室内机器人精准定位，无需依赖地面特征。
An indoor DSO-based ceiling-vision odometry system for indoor industrial environments
- 基于直接稀疏光流法（DSO）构建天花板视角里程计系统。
- 在真实工厂场景下实现亚米级定位误差，优于传统视觉里程计。
- 适用于各类无明显标记的天花板，适合工业移动机器人部署。
在工业室内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）能提供合理的状态估计，但传统方法在面对动态物体时表现不佳。通过向上拍摄天花板，可利用其静态、一致的空间特性进行位姿跟踪。本文提出基于直接稀疏光流（DSO）的天花板视觉里程计系统——Ceiling-DSO。与现有方案不同，该系统不依赖天花板上特定形状或特征点的假设，具有更广泛的适用性。由于缺乏公开的天花板视觉数据集，研究者在真实工业场景中构建了定制数据集，并用于评估该方法。通过调参优化，实现了接近真实值的在线姿态估计性能，定量和定性分析表明系统在复杂工业环境下具备实用性。
原文摘要 · Abstract (English)
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.
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