Reliability measure assignment to sonar for robust target differentiation

Cataloged from PDF version of article.This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target differentiation using sonar sensors. Classification of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically different sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster-Shafer evidential reasoning and majority voting, The sensing nodes view the targets at different ranges and angles so that they have different degrees of reliability. Proper accounting for these different reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classification performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassification. rate. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved

Comparative analysis of different approaches to target differentiation and localization with sonar

Cataloged from PDF version of article.This study compares the performances of di erent methods for the di erentiation and localization of commonly encountered features in indoor environments. Di erentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identi/cation, map building, navigation, obstacle avoidance, and target tracking. Di erent representations of amplitude and time-of-2ight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target di erentiation algorithm, Dempster–Shafer evidential reasoning, di erent kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classi/er, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and arti/cial neural networks. The neural networks are trained with di erent input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen’s self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect di erentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserve

Fuzzy clustering and enumeration of target type based on sonar returns

Cataloged from PDF version of article.The fuzzy c-means (FCM) clustering algorithm is used in conjunction with a cluster validity criterion, to determine the number of different types of targets in a given environment, based on their sonar signatures. The class of each target and its location are also determined. The method is experimentally verified using real sonar returns from targets in indoor environments. A correct differentiation rate of 98% is achieved with average absolute valued localization errors of 0.5 cm and 0.8degrees in range and azimuth, respectively. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved

Neural networks for improved target differentiation and localization with sonar

Cataloged from PDF version of article.This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-¯ight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating±shrinking algorithms are used to incorporate learning in the identi®cation of parameter relations for target primitives. Networks trained with the generating±shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61±90%) employing multiple sensor nodes. A sensor node is a pair of transducers with ®xed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain suf®cient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can ®nd application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identi®cation, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. q 2001 Elsevier Science Ltd. All rights reserved

Pedestrian dead reckoning employing simultaneous activity recognition cues

Cataloged from PDF version of article.We consider the human localization problem using body-worn inertial/magnetic sensor units. Inertial sensors are characterized by a drift error caused by the integration of their rate output to obtain position information. Because of this drift, the position and orientation data obtained from inertial sensors are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user can provide information about his position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing localization simultaneously with activity recognition, we detect the activity context switches and use the corresponding position information as position updates in a localization filter. The localization filter also involves a smoother that combines the two estimates obtained by running the zero-velocity update algorithm both forward and backward in time. We performed experiments with eight subjects in indoor and outdoor environments involving walking, turning and standing activities. Using a spatial error criterion, we show that the position errors can be decreased by about 85% on the average. We also present the results of two 3D experiments performed in realistic indoor environments and demonstrate that it is possible to achieve over 90% error reduction in position by performing localization simultaneously with activity recognition

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Cataloged from PDF version of article.Three-dimensional models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, facility management, water management, environmental/industrial/urban planning and documentation. A 3-D model is typically composed of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. We propose a novel compression technique based on compressive sampling applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques. The proposed method offers a small compression ratio and provides a reasonable compromise between the reconstruction error and processing time

Differentiation and localization of targets using infrared sensors

Cataloged from PDF version of article.This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings obtained with such systems are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6 , respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than that which they are commonly employed for. (C) 2002 Elsevier Science B.V. All rights reserve

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Cataloged from PDF version of article.Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded

Location and curvature estimation of spherical targets using multiple sonar time-of-flight measurements

A novel, flexible, three-dimensional multisensor sonar system is described to localize the center of a generalized spherical target and estimate its radius of curvature. Point, line, and planar targets are included as limiting cases which are important for the characterization of a mobile robot's environment. Sensitivity analysis of the curvature estimate with respect to measurement errors and some of the system parameters is provided. The analysis is verified experimentally for specularly reflecting cylindrical and planar targets. Typical accuracies in range and azimuth are 0.17 mm and 0.1°, respectively. Accuracy of the curvature estimate depends on the target type and system parameters such as transducer separation and operating range

Directional processing of ultrasonic arc maps and its comparison with existing techniques

A new technique for processing ultrasonic arc maps is proposed and compared to six existing techniques for map-building purposes. These techniques are simple point marking along the line-of-sight, voting and thresholding, morphological processing, Bayesian update scheme for occupancy grids, arc-transversal median algorithm, and triangulation-based fusion. The directional maximum technique, newly proposed in this paper, employs directional processing to extract the map of the environment from ultrasonic arc maps. It aims at overcoming the intrinsic angular uncertainty of ultrasonic sensors in map building, as well as eliminating noise and cross-talk related misreadings. The compared techniques are implemented with a wall-following motion-planning scheme for ground coverage. The comparison is based on experimental data and three complementary error criteria: mean absolute error, correct detection rate for full and empty regions, and computational cost in terms of CPU time. The directional maximum technique offers a very good compromise between mean absolute error and correct detection rate, with a processing time less than one-tenth of a second. Compared to the existing techniques, the directional maximum method is also superior in range accuracy and in eliminating artifacts, resulting in the best overall performance. The results indicate several trade-offs in the choice of ultrasonic arc-map processing techniques. © 2007 SAGE Publications