This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.

/pdf/orb-slam-a-versatile-and-accurate-monocular-slam-system-t5f1c0a74e.pdf

ORB-SLAM: A Versatile and Accurate Monocular SLAM System

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

/pdf/scikit-image-image-processing-in-python-3tlzmiwc32.pdf

scikit-image: Image processing in Python

This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.

/pdf/orb-slam-a-versatile-and-accurate-monocular-slam-system-rnvdngj54a.pdf

ORB-SLAM: a Versatile and Accurate Monocular SLAM System

We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/msracver/Deformable-ConvNets.

Deformable Convolutional Networks

An example method includes receiving, by a mobile robotic device, power from a battery of a first battery pack in order to operate within an environment. The method further includes establishing a first data channel between the mobile robotic device and the first battery pack. The method also includes using the first data channel to transfer sensor data acquired by the mobile robotic device during operation to a local data storage component of the first battery pack. The method additionally includes navigating, by the mobile robotic device, to a battery exchange station to transfer the first battery pack containing the battery and the local data storage component with the sensor data to the battery exchange station. The method further includes after transferring the first battery pack to the battery exchange station, receiving a second battery pack from the battery exchange station to continue operation within the environment.

Battery exchange station, battery pack and method of using robots at battery exchange station

A control system may perform functions including (i) storing data indicating an association between an optical identifier and a first robot, (ii) sending, to the first robot, data encoding the optical identifier for display by the first robot, and (iii) after sending the data encoding the optical identifier, sending, to a second robot, the data indicating the association between the optical identifier and the first robot. In some examples, the first robot may receive, from the control system, data encoding a second optical identifier of the first robot so that the first robot may display the second optical identifier instead of the first optical identifier. In some examples, a first robot may capture an image of an indication of a priority status of a second robot and perform an action based on comparing a first priority status of the first robot to the second priority status of the second robot.

Control system, robot and robot system

로봇 디바이스들에 대한 안전 규칙들을 명시하기 위한 시스템들 및 방법들이 제공된다. 컴퓨팅 디바이스는 환경의 미리 결정된 구역 내에 존재하는 임의의 행위자들에 관한 정보를 결정할 수 있다. 컴퓨팅 디바이스는 정보에 기초하여 미리 결정된 구역에 대한 안전 분류를 결정할 수 있다. 안전 분류는: 정보가 미리 결정된 구역 내에 어떠한 행위자들도 존재하지 않는다는 것을 나타내는 경우의 하위 안전 분류, 정보가 미리 결정된 구역 내에 존재하는 임의의 행위자들 전부가 미리 결정된 제1 유형이라는 것을 나타내는 경우의 중간 안전 분류, 및 정보가 미리 결정된 구역 내에 존재하는 적어도 하나의 행위자가 미리 결정된 제2 유형이라는 것을 나타내는 경우의 상위 안전 분류를 포함할 수 있다. 미리 결정된 구역에 대한 안전 분류를 결정한 후에, 컴퓨팅 디바이스는 미리 결정된 구역 내에서 작동하는 것에 대한 안전 규칙을 환경에서 작동하는 로봇 디바이스에 제공할 수 있다.

인간/로봇 통합 환경들에 대한 안전 시스템

To provide a system and a method for designating a safety rgulation concerning a robot device.SOLUTION: A computing device may determine information concerning arbitrary actors existing in a prescribed area in an environment. The computing device may determine safety classes for the prescribed area on the basis of the information. The safety classes may include: a low safety class at which the information shows that actors do not exist in the prescribed area; a middle safety class at which the information shows that the arbitrary actors exist in the prescribed area and all of the actors are prescribed first types; and a high safety class at which the information shows that at least one actor existing in the prescribed area is a prescribed second type. After determining the safety classes related to the prescribed area, the computing device may provide a robot device that operates in the environment with the safety regulation concerning operation in the prescribed area.SELECTED DRAWING: Figure 7

Ethan Rublee

Papers

Battery exchange station, battery pack and method of using robots at battery exchange station

Control system, robot and robot system

인간/로봇 통합 환경들에 대한 안전 시스템

Safety system for human/robot integration environment