TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization

Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be divided into two parts. During message transmission, one part is transmitted through a socket and the other part uses shared memory. The part within shared memory is never copied or serialized during its lifetime. We have integrated TZC with ROS and ROS2 and find that TZC can be easily combined with current open-source platforms. By using TZC, the overhead of IPC remains constant when the message size grows. In particular, when the message size is 4MB (less than the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2 IPC from hundreds of milliseconds to less than 1 millisecond. We also demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in autonomous driving scenarios. We show that by using TZC, the braking distance can be shortened by 16% than ROS

Опыт постижения истории, как духовного развития человечества

В статье рассматриваются методологические основы формирования концепции истории духовного развития человечества. Делается вывод об ограниченности монистического подхода к анализу истории становления человеческого духа, как материалистического, так и идеалистического. Рассматриваются возможности применения дуалистического подхода, основанного на принципах единства духовного и материального (дух вне материи не существует, материя вне духа бессмысленна), раскрытия их взаимодействия в глобальных противоречиях эпохи и снятия их в процессе цивилизованного переустройства мира. Рассматриваются гуманистические аспекты цивилизации, как наивысшей формы "культурной общности", "способа существования человеческого разума во Вселенной", раскрытия и обретения свободы в преобразовании мира. На основе продвижения человечества от космогенной – к техногенной, и от неё – антропогенной цивилизации, выделяется типы традиционной, инновационной и либеральной духовности. Делается вывод о том, что кризис либерального типа духовности предполагает формирование интеллектуально-нравственного типа духовности, как духовности современного технотронного общества

3D indoor scene modeling from RGB-D data: a survey

3D scene modeling has long been a fundamental problem in computer graphics and computer vision. With the popularity of consumer-level RGB-D cameras, there is a growing interest in digitizing real-world indoor 3D scenes. However, modeling indoor 3D scenes remains a challenging problem because of the complex structure of interior objects and poor quality of RGB-D data acquired by consumer-level sensors. Various methods have been proposed to tackle these challenges. In this survey, we provide an overview of recent advances in indoor scene modeling techniques, as well as public datasets and code libraries which can facilitate experiments and evaluation

Static scene illumination estimation from video with applications

We present a system that automatically recovers scene geometry and illumination from a video, providing a basis for various applications. Previous image based illumination estimation methods require either user interaction or external information in the form of a database. We adopt structure-from-motion and multi-view stereo for initial scene reconstruction, and then estimate an environment map represented by spherical harmonics (as these perform better than other bases). We also demonstrate several video editing applications that exploit the recovered geometry and illumination, including object insertion (e.g., for augmented reality), shadow detection, and video relighting

S4Net: Single Stage Salient-Instance Segmentation

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320x320). We evaluate our approach on a publicly available benchmark and show that it outperforms other alternative solutions. We also provide a thorough analysis of the design choices to help readers better understand the functions of each part of our network. The source code can be found at \url{https://github.com/RuochenFan/S4Net}