Fast view-based pose estimation of industrial objects in point clouds using a particle filter with an ICP-based motion model

The registration of an observed point set to a known model to estimate its 3D pose is a common task for the autonomous manipulation of objects. Especially in industrial environments, robotic systems need to accurately estimate the pose of objects in order to successfully perform picking, placing or assembly tasks. However, the characteristics of industrial objects often cause difficulties for classical pose estimation algorithms, especially when using IR depth sensors. In this work, we propose to solve ambiguities of the pose estimate by representing the it as a virtual view on a reference model within an adapted particle filter system. Therefore, a simple but fast method to cast views from the reference model is presented, making a training phase obsolete while increasing the accuracy of the estimate. The view-based approach increases the robustness of the registration process and reformulates the pose estimation as a problem of determining the most likely view using a particle filter. By incorporating a local optimizer (ICP) into the dynamics model of the particle filter, the proposed method directs the search in the 6-dimensional pose space, thereby reducing the amount of needed particles to about 50 while decreasing the convergence time to a minimum and therefore making it viable for real-time pose estimation. In contrast to other pose estimation methods, this approach explores the possibilities of sequential pose estimation by only using plain point clouds without additional features.