Robotics (cs.RO)

Abstract: Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but pose estimation is difficult on cluttered objects. In this paper, we propose Cluttered Objects Descriptors (CODs), a dense cluttered objects descriptor that can represent rich object structures, and use the pre-trained CODs network along with its intermediate outputs to train a picking policy. Additionally, we train the policy with reinforcement learning, which enable the policy to learn picking without supervision. We conduct experiments to demonstrate that our CODs is able to consistently represent seen and unseen cluttered objects, which allowed for the picking policy to robustly pick cluttered general objects. The resulting policy can pick 96.69% of unseen objects in our experimental environment which is twice as cluttered as the training scenarios.

Abstract: A global navigation satellite system (GNSS) is a sensor that can acquire 3D position and velocity in an earth-fixed coordinate system and is widely used for outdoor position estimation of robots and vehicles. Various GNSS/inertial measurement unit (IMU) integration methods have been proposed to improve the accuracy and availability of GNSS positioning. However, all of them require the addition of a 3D attitude to the estimated state in order to fuse the IMU data. This study proposes a new optimization-based positioning method for combining GNSS and IMU that does not require attitude estimation. The proposed method uses two types of constraints: one is a constraint between states using only the magnitude of the 3D acceleration observed by an accelerometer, and the other is a constraint on the angle between the velocity vectors using the amount of angular change by a gyroscope. The evaluation results with simulation data show that the proposed method maintains the position estimation accuracy even when the IMU mounting position error increases and improves the accuracy when the GNSS observations contain multipath errors or missing data. The proposed method could improve the positioning accuracy in experiments using IMUs acquired in real environments.

Abstract: Automated driving systems are safety-critical cyber-physical systems whose safety of the intended functionality (SOTIF) can not be assumed without proper argumentation based on appropriate evidences. Recent advances in standards and regulations on the safety of driving automation are therefore intensely concerned with demonstrating that the intended functionality of these systems does not introduce unreasonable risks to stakeholders. In this work, we critically analyze the ISO 21448 standard which contains requirements and guidance on how the SOTIF can be provably validated. Emphasis lies on developing a consistent terminology as a basis for the subsequent definition of a validation strategy when using quantitative acceptance criteria. In the broad picture, we aim to achieve a well-defined risk decomposition that enables rigorous, quantitative validation approaches for the SOTIF of automated driving systems.

Abstract: Nowadays, unmanned aerial vehicles or UAVs are being used for a wide range of tasks, including infrastructure inspection, automated monitoring and coverage. This paper investigates the problem of 3D inspection planning with an autonomous UAV agent which is subject to dynamical and sensing constraints. We propose a receding horizon 3D inspection planning control approach for generating optimal trajectories which enable an autonomous UAV agent to inspect a finite number of feature-points scattered on the surface of a cuboid-like structure of interest. The inspection planning problem is formulated as a constrained open-loop optimal control problem and is solved using mixed integer programming (MIP) optimization. Quantitative and qualitative evaluation demonstrates the effectiveness of the proposed approach.

Abstract: Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches. Thus, engineers need to fully understand their large image data sets for the development and test of machine learning algorithms. However, current approaches lack automatability, are not generic and are limited in their expressiveness. Hence, this paper aims to analyze a state-of-the-art text and image embedding neural network and guides through the application in the automotive domain. This approach enables the search for similar images and the search based on a human understandable text-based description. Our experiments show the automatability and generalizability of our proposed method for handling large data sets in the automotive domain.