Software Framework for State Estimation

Over the past decade, robotics has seen tremendous increase in complexity and variety of applications. The key area in the robots seeing rapid evolution is the software. However, usually the software developed for robots has been limited to a specific application and/or a specific hardware. Unfortunately most of the software developed for robotic applications are not easily re-usable in another project. Very little effort has been done to tackle this issue and the software is developed on an ad-hoc basis. In this work, a framework for developing sensor fusion software is proposed that is based on practices of model-driven engineering. A small domain-specific language is developed that effectively hides the lower level implementation details and makes the software development more structured and easier to re-use. It is also discussed how graphical models can be used as computational framework for performing the statistical inference in filtering problems. It is shown how a simple estimation problem can be solved using graphical models

3D Object Detection and Tracking Based On Point Cloud Li- brary Special Application In Pallet Picking For Autonomous Mobile Machines

This work covers the problem of object recognition and pose estimation in a point cloud data structure, using PCL (Point Cloud Library). The result of the computation will be used for mobile machine pallet picking purposes, but it can also be applied to any context that requires finding and aligning a specific pattern. The goal is to align an object model to the visible instances of it in an input cloud. The algorithm that will be presented is based on local geometry descriptors that are computed on a set of uniform key points of the point clouds. Correspondences (best matches) between such features will be filtered and from this data comes a rough alignment that will be refined by ICP algorithm. Robust dedicated validation functions will guide the entire process with a greedy approach. Time and effectiveness will be discussed, since the target industrial application imposes strict constraints of performance and robustness. The result of the proposed solution is really appreciable, since the algorithm is able to recognize present objects, with a minimal percentage of false negatives and an almost zero false positives rate. Experiments have been conducted on datasets acquired from a state-of-the-art simulator and some sample scene from the real environment