Refine
Year of publication
Document Type
- Conference Proceeding (25) (remove)
Is part of the Bibliography
- no (25)
Keywords
- Augmented Reality (AR) (3)
- Robotic Research (3)
- Cognitive Mapping (2)
- Mobile Robot (2)
- Pattern Recognition (2)
- Sonar Sensor (2)
- 3D Point-Sets (1)
- 3D Rotations (1)
- Approximate dynamic program (1)
- Automation and Mechatronics (1)
Institute
Parameter free Non-intrusive Load Monitoring (NILM) algorithms are a major step toward real-world NILM scenarios. The identification of appliances is the key element in NILM. The task consists of identification of the appliance category and its current state. In this paper, we present a param- eter free appliance identification algorithm for NILM using a 2D representation of time series known as unthresholded Recurrence Plots (RP) for appliance category identification. One cycle of voltage and current (V-I trajectory) are transformed into a RP and classified using a Spacial Pyramid Pooling Convolutional Neural Network architecture. The performance of our approach is evaluated on the three public datasets COOLL, PLAID and WHITEDv1.1 and compared to previous publications. We show that compared to other approaches using our architecture no initial parameters have to be manually tuned for each specific dataset.
Stuttering is a complex speech disorder identified by repetitions, prolongations of sounds, syllables or words and blockswhile speaking. Specific stuttering behaviour differs strongly,thus needing personalized therapy. Therapy sessions requirea high level of concentration by the therapist. We introduce STAN, a system to aid speech therapists in stuttering therapysessions. Such an automated feedback system can lower the cognitive load on the therapist and thereby enable a more consistent therapy as well as allowing analysis of stuttering over the span of multiple therapy sessions.
We present an approach for indoor mapping and localization with a mobile robot using sparse range data, without the need for solving the SLAM problem.
The paper consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework.
Spatial information extracted from this map is then used for self-localization. The robot computes local confidence maps for two simple localization strategies based on distance and relative orientation of regions.
The local confidence maps are then fused using an approach adapted from computer vision to produce overall confidence maps. Experiments on data acquired by mobile robots equipped with sonar sensors are presented.
We present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. The initial metric map is obtained from range data that are converted to a geometric map consisting of linear approximations of the indoor environment.
The splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends).
A regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. Experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method.
This paper presents a novel algorithm for computing absolute space representations (ASRs) in Yeap, W.K. and Jefferies, M. (1988) for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown
This paper shows how a mobile robot equipped with sonar sensors and an odometer is used to test ideas about cognitive mapping. The robot first explores an office environment and computes a "cognitive map" which is a network of ASRs [1]. The robot generates two networks, one for the outward journey and the other for the journey home.
It is shown that both networks are different. The two networks, however, are not merged to form a single network. Instead, the robot attempts to use distance information implicit in the shape of each ASR to find its way home. At random positions in the homeward journey, the robot calculates its orientation towards home. The robot's performances for both problems are evaluated and found to be surprisingly accurate.
When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home.
Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information.
Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used for localization?
In this paper, we present an approach using a mobile robot to generate a “cognitive map”, the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a “cognitive map” and uses distance and orientation information for localization.
When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to developa varietyof methodsthat tend to utilize two key pieces of information,namely distance and orientation information.
Their methods differ depending on how they sense their environment.
Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used to return home?
In this paper, we presented a novel approach using a mobile robot to do cognitive mapping. Our robot computes a “cognitive map” and uses distance and orientation information to find its way home.
The process developed provides interesting insights into the nature of cognitive mapping and encourages us to use a mobile robot to do cognitive mapping in the future, as opposed to its popular use in robot mapping.
In this paper, we present our experience in designing and teaching of our first robotics course for students at primary school level.
The course was carried out over a comparatively short period of time, namely 6 weeks, 2 hours per week. In contrast to many other projects, we use robots that researchers used to conduct their research and discuss problems faced by these researchers. Thus, this is not a behavioural study but a hands-on learning experience for the students.
The aim is to highlight the development of autonomous robots and artificial intelligence as well as to promote science and robotics in schools.
We present an Approach for non linea roptimization of the parameters of an endoscopic camera mounted on a surgery robot. The goal is to generate a depth map for each image in order to enhance the quality of medical light fields.
The pose information provided by the robot is used as an initialization, where especially the orientation isi naccurate. Refinement of intrinsic and extrinsic camera parameters is performed by minimizing the back-projectionerror of 3-D points that are reconstructed by triangulation from image Feature stracked over an image sequence.
Optimization of the camera parameters results in an enhancement of Rendering Quality in two ways: More accurate parameters lead to better interpolation as well as to better depth maps for approximating the scenegeometry.
This work presents a technique for computing dense disparity maps from a binocular stereo camera system. The methods are applied in an Augmented Reality setting for combining real and virtual worlds with proper occlusions. The proposed stereo correspondence technique is based oil area matching and facilitates an efficient strategy by using the concept of a three-dimensional similarity accumulator whereby occlusions are detected and object boundaries are extracted correctly. The main contribution of this paper is the way we fill the accumulator using absolute differences of images and computing a mean filter on these difference images. This. is. where the main advantages of the accumulator approach can be exploited, since all entries can be computed in parallel and thus extremely efficient. Additionally, we-perform an asymmetric correction step and a post-processing of the disparity maps that maintains object edges.
In this paper we address the problem of using quaternions in unconstrained nonlinear optimization of 3-D rotations. Quaternions representing rotations have four elements but only three degrees of freedom, since they must be of norm one.
This constraint has to be taken into account when applying e.g. the Levenberg-Marquardt algorithm, a method for unconstrained nonlinear optimization widely used in computer vision. We propose an easy to use method for achieving this.
Experiments using our parametrization in photo grammetric bündle -adjustment are presented at the end of the paper.
We describe an Augmented Reality system using the corners of a color cube for camera calibration. In the augmented image the cube is replaced by a computer generated virtual object.
The cube is localized in an image by the CSC color segmentation algorithm. The camera projection matrix is estimated with a linear method that is followed by a nonlinear refinement step.
Because of possible missclassifications of the segmented color regions and the minimum number of point correspondences used for calibration, the estimated pose of the cube may be very erroneous for some frames; therefore we perform outlier detection and treatment for rendering the virtual object in an acceptable manner.
Es wird ein System aus der Augmented Reality (Erweiterte Realiäat, AR) vorgestellt, das zur Kamerakalibrierung einen farbigen Würfel verwendet, der außerdem als Platzhalter für ein virtuelles Objekt dient.
Zur Erkennung des Würfels in einer Szene werden Methoden aus der Farbbildverarbeitung verwendet, wie der Color Structure Code (CSC) und die Klassifikation der entstehenden Regionen nach ihrer Farbe. Zur Beschleunigung der Segmentierung wird ein hierarchisches Verfahren eingesetzt.
There are many nearest neighbor algorithms tailor made for ICP,but most of them require Special input data like range Images or triangle meshes.
We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3-D point sets generated by structure-frommotion techniques. We shortly present the evaluated algorithms and introduce the modifications we made to improve their efficiency.
In particular, several enhancements to the well-known k-D tree algorithm are described. The first part of our Performance Analysis consists of Experiments on synthetic point sets, whereas the second part features experiments with the ICP algorithm on real point sets. Both parts are completed by a thorough evaluation of the obtained results.
Dieser Artikel stellt ein Konzept f¨ur eine Online-Vorlesung im Rahmen der virtuellen Hochschule Bayern vor.
Zunächst werden kurz die fachlichen Inhalte erl¨autert, die es zu vermitteln gilt.
Nach dieser Einführung wird auf die Umsetzung der Inhalte in eine dem neuen Medium Internet angemessene Form eingegangen.
Dies betrifft vor allem die ausgewählten Techniken, die zur Wissensvermittlung eingesetzt werden.
Ein besonderer Schwerpunkt liegt auf der Möglichkeit durch Interaktivität das Wissen experimentell zu vertiefen und somit eine Verbindung zwischen dem theoretisch Gelernten und dem praktisch Erfahrenen herzustellen.
Weiterhin wird eine Vielfalt an Kommunikationsm¨oglichkeiten vorgestellt, da Kommunikation eine wichtige soziale Komponente im Lernprozess und h¨aufig entscheidend f¨ur den Erfolg ist.
Robust registration of two 3-D point sets is a common problem in computer vision.
The iterative closest point (ICP) algorithm is undoubtedly the most popular algorithm for solving this kind of problem. In this paper, we present the Picky ICP algorithm, which has been created by merging several extensions of the standard ICP algorithm, thus improving its robustness and computation time.
Using pure 3-D point sets as input data, we do not consider additional information like point color or neighborhood relations. In addition to the standard ICP algorithm and the Picky ICP algorithm proposed in this paper, a robust algorithm due to Masuda and Yokoya and the RICP algorithm by Trucco et al. are evaluated.
We have experimentally determined the basin of convergence, robustness to noise and outliers, and computation time of these four ICP based algorithms
The paper presents an extended hand-eye calibration approach that, in contrast to the standard method, does not require a calibration pattern for determining camera position and orientation. Instead, a structure-from-motion algorithm is applied for obtaining the eye-data that is necessary for computing the unknown hand-eye transformation.
Different ways of extending the standard algorithm are presented, which mainly involves the estimation of a scale factor in addition to rotation and translation. The proposed methods are experimentally compared using data obtained from an optical tracking system that determines the pose of an endoscopic camera.
The approach is of special interest in our clinical setup, as the usage of an unsterile calibration pattern is difficult in a sterile environment.
Wepresentaniterativeregistrationalgorithmfor aligning two differently scaled 3-D point sets. It extends the popular Iterative Closest Point (ICP) algorithm by estimating a scale factor between the two point sets in every iteration.
The presented algorithm is especially useful for the registration of point sets generated by structure-frommotion algorithms, which only reconstruct the 3-D structure of a scene upto scale. LiketheoriginalICPalgorithm,thepresentedalgorithm requires a rough pre-alignment of the point sets.
In order to determine the necessary accuracy of the pre-alignment, wehaveexperimentallyevaluatedthebasinofconvergence of the algorithm with respect to the initial rotation, translation, andscale factor between the two point sets.
The paper presents a new vectorquantization based Approach for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements.
Data selection is essential if control of the movements used for calibration is not possible, especially when using continuously recorded data. The new algorithm is compared to another method for data selection as well as to the processing of subsequent movements.
Experimental results on real and synthetic data sets show the superior performance of the new approach with respect to calibration errors and computation time.
Real data has been obtained from an optical tracking system and a camera mounted on an endoscope, the goal being the reconstruction of medical lightfields.