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In supervised machine learning applications, a data set of training and validation features and labels is required to train a neural network. In this paper, we present a remote-controlled, mobile robot and describe software used to generate a data set for vision-based, supervised machine learning applications. We present results from an experiment, which validates the developed platform, and also...
Scale recovery is one of the central problems for monocular visual odometry. Normally, road plane and camera height are specified as reference to recover the scale. The performances of these methods depend on the plane recognition and height measurement of camera. In this work, we propose a novel method to recover the scale by incorporating the depths estimated from images using deep convolutional...
Camera calibration is essential for accurate computer vision, and automatic calibration of some extrinsic parameters is needed in case the camera is placed on a mobile platform. The pitch and yaw angles, which are the most likely ones to change as the vehicle moves, can be inferred from the image coordinates of the vanishing point (VP). In this paper we present an artificial neural network approach...
A robot needs to localize an unknown object before grasping it. When the robot only has a monocular sensor, how can it get the object pose? In this work, we present a method of localizing the 6-DOF pose of a target object using a robotic arm and a hand-mounted monocular camera. The method includes an object recognition and a localization process. The recognition process uses point features on a surface...
Recognition is always an interesting aspect of visual processing, especially for systems that requires intuitive perception like robotics or human-machine interactions. In this work, a color recognition system based on Evidence Theory is applied for a scenario of the NAO robot that recognizes the color of a requested ball. The robot employs multi-cameras to reduce uncertainties, and the Dempster-Shafer...
This paper describes the current status of the development of a simulation system for remotely operated robots. The system will be used for operator proficiency training and robot performance verification. Our purpose for developing this system is to contribute to decommissioning of the Fukushima Daiichi Nuclear Power Station (FDNPS). The simulator system was designed using Choreonoid, a simulator...
Convolutional Neural Networks (CNNs) have been applied to camera relocalization, which is to infer the pose of the camera given a single monocular image. However, there are still many open problems for camera relocalization with CNNs. We delve into the CNNs for camera relocalization. First, a variant of Euler angles named Euler6 is proposed to represent orientation. Then a data augmentation method...
This paper describes the development of a specialized application for voice command recognition for the Jaguar V4 robot in conjunction with the Starkville, MS, USA Special Weapons and Tactics (SWAT) team during training. This training took place at The Center for Advanced Vehicular Systems (CAVS), which provides a specialized environment for police SWAT training. This reconfigurable space, setup during...
This paper presents an object recognition modular system implementation that allows a humanoid robot to recognize and manipulate objects in a service robotics context. The system consists of three modules: I) Object recognition, II) Object manipulation and III) Humanoid-robot interaction. The Object recognition uses A-KAZE descriptor and Growing Cell Structure (GCS) artificial neural network (ANN)...
Convolutional Neural Networks (CNNs) have achieved great successes in many computer vision tasks and have been applied to pose regression for camera relocalization. Traditional Simultaneously Localization and Mapping (SLAM) approaches use correspondences between camera coordinates and world coordinates to estimate camera pose. In this paper, we present a new camera relocalization method including...
With the advent of commodity autonomous mobiles, it is becoming increasingly prevalent to recognize under extreme conditions such as night, erratic illumination conditions. This need has caused the approaches using multi-modal sensors, which could be complementary to each other. The choice for the thermal camera provides a rich source of temperature information, less affected by changing illumination...
This paper is focused on the problem of tracking an object by the head movement of robot with two cameras simultaneously, one robot camera and one fixed external camera. The goal of using external camera is to test, how it can aid the head camera of robot when the object moves out of its field of view. The setup of system is focused on comparison of robot with and without additional camera. The tracked...
Omnidirectional cameras are commonly used in computer vision and robotics. Their main advantage is their wide field of view which allows them to acquire a 360 degree view of the scene with only one sensor and a single shot. However, few studies have investigated the human detection problem using this kind of cameras. In this paper, we propose to extend the conventional approach for human detection...
Understanding semantic meaning from hand gestures is a challenging but essential task in human-robot interaction scenarios. In this paper we present a baseline evaluation of the Innsbruck Multi-View Hand Gesture (IMHG) dataset [1] recorded with two RGB-D cameras (Kinect). As a baseline, we adopt a probabilistic appearance-based framework [2] to detect a hand gesture and estimate its pose using two...
As robots enter human environments, they will be expected to accomplish a tremendous range of tasks. It is not feasible for robot designers to pre-program these behaviors or know them in advance, so one way to address this is through end-user programming, such as learning from demonstration (LfD). While significant work has been done on the mechanics of enabling robot learning from human teachers,...
In this work we propose an architecture for fully automated person re-identification in camera networks. Most works on re-identification operate with manually cropped images both for the gallery (training) and the probe (test) set. However, in a fully automated system, re-identification algorithms must work in series with person detection algorithms, whose output may contain false positives, detections...
In this paper, we explore a new algorithm to detect people with thermal cameras based on the standard Implicit Shape Model (ISM) technique. Our approach starts with the ISM to define the proposed centers of people locations. Then we utilize a novel method to detect people based on the density of the concentrated proposed centers by using an auto generated threshold mechanism. Our method is easy to...
In this paper we apply light field reconstruction and rendering of object views to the problem of automatic generation of training material for a statistical object recognition system. The advantages of using a light field instead of real images are shown. We evaluate with respect to the error rate of the classifier, whether the reconstructed light field can be applied to the training step. We also...
Advancements in Virtual Reality have enabled well-defined and consistent virtual environments that can capture complex scenarios, such as human everyday activities. Additionally, virtual simulators (such as SIGVerse) are designed to be user-friendly mechanisms between virtual robots/agents and real users allowing a better interaction. We envision such rich scenarios can be used to train robots to...
In this paper, we present Roman Tutor, an intelligent tutoring simulator to train astronauts on manipulating the SSRMS, an articulated robot arm deployed on the International Space Station. Roman Tutor incorporates a model of the system operations curriculum, a kinematic simulation of the robotics equipment and the ISS, a high performance path planner and an automatic task demonstration generator...
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