Multi-robot global localization (MR-GL) with unknown initial positions in a large scale environment is a challenging task. The key point is the data association between different robots’ viewpoints. It also makes traditional Appearance-based localization methods unusable. Recently, researchers have utilized the object’s semantic invariance to generate a semantic graph to address this issue. However, previous works lack robustness and are sensitive to overlap rate of maps, resulting in unpredictable performance in real-world environments. In this paper, we propose a data association algorithm based on neighbor constraints to improve the robustness of the system. We demonstrate the effectiveness of our method on three different datasets, indicating a significant improvement in robustness compared to previous works.

Abstract In this paper, a new Hough transform is proposed to detect lines with geometric meanings. In the standard Hough transform, lines are parameterized by the length and orientation of the normal vector from the origin to the line. The parameter space of the standard Hough transform is presented in a polar coordinate system. Geometric measurements such as distance and angle are not suitable in polar coordinate space due to the inconsistency of the axes. To deal with this problem, a Hough transform which is carried out in a bounded Cartesian coordinate parameter space is developed. The lines are represented by their perpendicular feet with respect to a fixed point. Since the parameter space is in Cartesian coordinate space, it is intuitive to the researchers. When detecting the peaks in the parameter space, distance and angle constraints can be applied. The effectiveness of the proposed method has been illustrated by two applications, that is, lane line detection and convex polygon extraction. The results show that the proposed Cartesian coordinate Hough transform is suitable for detecting meaningful lines.

Machine learning has made breakthroughs in areas such as computer vision and natural language processing. In recent years, more and more research has been done on the application of machine learning on robotic grasping. This article summarizes the research progress of machine learning on robotic grasping, from the aspects of object grasping datasets, two main categories of methods that differ from the criteria for successful grasping with deep learning or reinforcement learning algorithm, discusses what current researches have done and the problems that have not yet been solved, and hopes to inspire new ideas in research of robotic grasping based on machine learning.