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In recent years, autonomous robots have been practically used outdoors for transportation, delivery, and other applications. To ensure safety and reliability, it is necessary to measure and evaluate the accuracy of a robot's movement from the outside. In this study, we develop a device to externally measure the coordinates and azimuth angle of a robot by combining image processing and distance measurement. In this study, an azimuth angle detection method that combines coarse-angle detection using AKAZE feature detection and small-angle detection using optical flow is proposed. The experimental results show that the azimuth angle can be detected with a standard deviation of σ=0.70° indoors and σ=0.99° outdoors. [ABSTRACT FROM AUTHOR]

Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow. [ABSTRACT FROM AUTHOR]

All terrain cranes often work in construction sites. Blind spots, limited information, and high mental workload are problems encountered by crane operators. A top-view camera mounted on the boom head offers a valuable perspective on the workspace that can help eliminate blind spots and provide the basis for assisting operation. In this study, a visual 2D map of a crane workspace is generated from images captured by a top-view camera. Various types of information can be overlaid on this visual 2D map to assist the operator, such as recording the operation and projecting the boom head's expected path through the workspace. Herein, the process of generating a visual map by stitching and locating the boom head trajectory in that visual map is described. Preliminary proof-of-concept tests show that a precise map and projected trajectories can be generated via image-processing techniques that discriminate foreground objects from the scene below the crane. The location error is analyzed and verified to confirm its applicability. These results show a way to help the operator make more precise operation easily and reduce the operator's mental workload. [ABSTRACT FROM AUTHOR]

Mobile robots equipped with camera sensors are required to perceive surrounding humans and their actions for safe and autonomous navigation. In this work, moving humans are the target objects. For robot vision, real-time performance is an important requirement. Therefore, we propose a robot vision system in which the original images captured by a camera sensor are described by optical flow. These images are then used as inputs to a classifier. For classifying images into human and not-human classifications, and the actions, we use a convolutional neural network (CNN), rather than coding invariant features. Moreover, we present a local search window as a novel detector for clipping partial images around target objects in an original image. Through the experiments, we ultimately show that the robot vision system is able to detect moving humans and recognize action in real time. [ABSTRACT FROM AUTHOR]