Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Localization and tracking of parameterized objects in point clouds

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 43-46).This thesis focuses on object recognition and tracking from three dimensional point cloud renderings of dense range and bearing data. Sensors like laser range-finders and depth cameras have become increasingly popular in autonomous robotic applications. A common task is to locate and track specific objects of interest located somewhere in the point cloud. This often introduces a tedious network of heuristics to build objects from identified primitives or an intractable high dimensional search space. Through a parameterized object model and certain relaxation functions, a likelihood based view of the data can be used to accomplish these goals with increased performance and reliability. Improvements in mathematics and convergence properties have shown that this method can be realized in real time.by Robert Truax.S.M

Vergence control system for stereo depth recovery

This paper describes a vergence control algorithm for a 3D stereo recovery system. This work has been developed within framework of the project ROBTET. This project has the purpose of designing a Teleoperated Robotic System for live power lines maintenance. The tasks involved suppose the automatic calculation of path for standard tasks, collision detection to avoid electrical shocks, force feedback and accurate visual data, and the generation of collision free real paths. To accomplish these tasks the system needs an exact model of the environment that is acquired through an active stereoscopic head. A cooperative algorithm using vergence and stereo correlation is shown. The proposed system is carried out through an algorithm based on the phase correlation, trying to keep the vergence on the interest object. The sharp vergence changes produced by the variation of the interest objects are controlled through an estimation of the depth distance generated by a stereo correspondence system. In some elements of the scene, those aligned with the epipolar plane, large errors in the depth estimation as well as in the phase correlation, are produced. To minimize these errors a laser lighting system is used to help fixation, assuring an adequate vergence and depth extraction .The work presented in this paper has been supported by electric utility IBERDROLA, S.A. under project PIE No. 132.198

Distributed Robotic Vision for Calibration, Localisation, and Mapping

This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours. This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages

Figure-Ground Segmentation Using Multiple Cues

The theme of this thesis is figure-ground segmentation. We address the problem in the context of a visual observer, e.g. a mobile robot, moving around in the world and capable of shifting its gaze to and fixating on objects in its environment. We are only considering bottom-up processes, how the system can detect and segment out objects because they stand out from their immediate background in some feature dimension. Since that implies that the distinguishing cues can not be predicted, but depend on the scene, the system must rely on multiple cues. The integrated use of multiple cues forms a major theme of the thesis. In particular, we note that an observer in our real environment has access to 3-D cues. Inspired by psychophysical findings about human vision we try to demonstrate their effectiveness in figure-ground segmentation and grouping also in machine vision

Extrinsic Calibration and Ego-Motion Estimation for Mobile Multi-Sensor Systems

Autonomous robots and vehicles are often equipped with multiple sensors to perform vital tasks such as localization or mapping. The joint system of various sensors with different sensing modalities can often provide better localization or mapping results than individual sensor alone in terms of accuracy or completeness. However, to enable improved performance, two important challenges have to be addressed when dealing with multi-sensor systems. Firstly, how to accurately determine the spatial relationship between individual sensor on the robot? This is a vital task known as extrinsic calibration. Without this calibration information, measurements from different sensors cannot be fused. Secondly, how to combine data from multiple sensors to correct for the deficiencies of each sensor, and thus, provides better estimations? This is another important task known as data fusion. The core of this thesis is to provide answers to these two questions. We cover, in the first part of the thesis, aspects related to improving the extrinsic calibration accuracy, and present, in the second part, novel data fusion algorithms designed to address the ego-motion estimation problem using data from a laser scanner and a monocular camera. In the extrinsic calibration part, we contribute by revealing and quantifying the relative calibration accuracies of three common types of calibration methods, so as to offer an insight into choosing the best calibration method when multiple options are available. Following that, we propose an optimization approach for solving common motion-based calibration problems. By exploiting the Gauss-Helmert model, our approach is more accurate and robust than classical least squares model. In the data fusion part, we focus on camera-laser data fusion and contribute with two new ego-motion estimation algorithms that combine complementary information from a laser scanner and a monocular camera. The first algorithm utilizes camera image information to guide the laser scan-matching. It can provide accurate motion estimates and yet can work in general conditions without requiring a field-of-view overlap between the camera and laser scanner, nor an initial guess of the motion parameters. The second algorithm combines the camera and the laser scanner information in a direct way, assuming the field-of-view overlap between the sensors is substantial. By maximizing the information usage of both the sparse laser point cloud and the dense image, the second algorithm is able to achieve state-of-the-art estimation accuracy. Experimental results confirm that both algorithms offer excellent alternatives to state-of-the-art camera-laser ego-motion estimation algorithms