Evaluation of a novel DSO-based Indoor Ceiling-Vision Odometry System

Indoor localization for mobile industrial robots is a crucial step toward an autonomous system. A mobile robot needs a reliable and robust localization system to achieve its task autonomously. A reasonable estimate of the robot’s state can be achieved through Visual Odometry (VO); however, with dynamic objects in the scene, classical VO approaches need to detect and filter these moving objects. Alternatively, we can use an up-facing camera to track the movement with respect to the ceiling, which represents a static and invariant space. This paper presents Ceiling-DSO, an indoor ceiling-vision (CV) system based on Direct Sparse Odometry (DSO). We take advantage of the generic formulation of DSO to avoid making assumptions about the observable shapes or landmarks on the ceiling, making the method generic and applicable to multiple ceiling types. We built a ceiling-vision dataset in a real-world scenario; we then used it to test our approach with different DSO parameters to identify the best fit for robot pose estimation. This paper provides a qualitative and quantitative analysis of the obtained results that showed an acceptable error rate compared to the ground truth.