一种基于DSO的室内工业环境天花板视觉里程计系统
An indoor DSO-based ceiling-vision odometry system for indoor industrial environments
AI总结：
针对室内工业环境中传统视觉里程计受动态物体干扰的问题，提出基于直接稀疏里程计的Ceiling-DSO天花板视觉系统，创建自定义数据集评估，通过参数优化实现可靠定位。
摘要：
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）能够对机器人状态提供合理估计，但传统VO方法在面对场景中的动态物体时会遇到挑战。此外，可利用朝上的摄像头来跟踪机器人相对于天花板的运动，天花板代表了一个静态且一致的空间。本文介绍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利用DSO的通用公式，避免对天花板上可观测的形状或地标做出假设，这一方法确保了该方法适用于各种类型的天花板。由于不存在公开可用的天花板视觉数据集，我们在真实场景中创建了一个自定义数据集，并使用它来评估我们的方法。通过调整DSO参数，我们确定了在线位姿估计的最优参数，与真实值相比获得了可接受的误差率。本文对所得结果进行了定性和定量分析。
英文摘要：
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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