An indoor DSO-based ceiling-vision odometry system for indoor industrial environments
This work addresses localization challenges for autonomous mobile robots in indoor industrial settings, representing an incremental improvement by adapting an existing method to a specific scenario.
The authors tackled the problem of reliable localization for autonomous mobile robots in indoor industrial environments by developing Ceiling-DSO, a ceiling-vision odometry system based on Direct Sparse Odometry, which achieved acceptable error rates compared to ground truth in a custom real-world dataset.
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.