Billur Barshan

Inertial navigation systems for mobile robots

A low-cost solid-state inertial navigation system (INS) for mobile robotics applications is described. Error models for the inertial sensors are generated and included in an extended Kalman filter (EKF) for estimating the position and orientation of a moving robot vehicle. Two different solid-state gyroscopes have been evaluated for estimating the orientation of the robot. Performance of the gyroscopes with error models is compared to the performance when the error models are excluded from the system. Similar error models have been developed for each axis of a solid-state triaxial accelerometer and for a conducting-bubble tilt sensor which may also be used as a low-cost accelerometer. An integrated inertial platform consisting of three gyroscopes, a triaxial accelerometer and two tilt sensors is described. >

Convolution, filtering, and multiplexing in fractional Fourier domains and their relation to chirp and wavelet transforms

A concise introduction to the concept of fractional Fourier transforms is followed by a discussion of their relation to chirp and wavelet transforms. The notion of fractional Fourier domains is developed in conjunction with the Wigner distribution of a signal. Convolution, filtering, and multiplexing of signals in fractional domains are discussed, revealing that under certain conditions one can improve on the special cases of these operations in the conventional space and frequency domains. Because of the ease of performing the fractional Fourier transform optically, these operations are relevant for optical information processing.

Differentiating sonar reflections from corners and planes by employing an intelligent sensor

A multitransducer, pulse/echo-ranging system is described that differentiates corner and plane reflectors by exploiting the physical properties of sound propagation. The amplitudes and ranges of reflected signals for the different transmitter and receiver pairs are processed to determine whether the reflecting object is a plane or a right-angle corner. In addition, the angle of inclination of the reflector with respect to the transducer orientation can be measured. Reflected signal amplitude and range values, as functions of inclination angle, provide the motivation for the differentiation algorithm. A system using two Polaroid transducers is described that correctly discriminates between corners and planes for inclination angles within +or-10 degrees of the transducer orientation. The two-transducer system is extended to a multitransducer array, allowing the system to operate over an extended range. An analysis comparing processing effort to estimation accuracy is performed. >

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.

Optimal filtering with linear canonical transformations

Optimal filtering with linear canonical transformations allows smaller mean-square errors in restoring signals degraded by linear time- or space-variant distortions and non-stationary noise. This reduction in error comes at no additional computational cost. This is made possible by the additional flexibility that comes with the three free parameters of linear canonical transformations, as opposed to the fractional Fourier transform which has only one free parameter, and the ordinary Fourier transform which has none. Application of the method to severely degraded images is shown to be significantly superior to filtering in fractional Fourier domains in certain cases.

A bat-like sonar system for obstacle localization

An active wide-beam sonar system that mimics the sensor configuration of echolocating bats is described for applications in sensor-based robotics. Obstacles in a two-dimensional (2-D) environment are detected and localized using time-of-flight (TOF) measurements of their echoes. The standard threshold detector produces a biased TOF estimate. An unbiased TOF estimate is derived by a parametric fit to the echo waveform, motivated by experimental observations of actual sonar signals. This novel method forms a tradeoff between the complexity of the optimum estimator and the biased threshold detector. Using the TOF information from both methods, the range and azimuth of an obstacle are estimated. Localization is most accurate if the obstacle is located along the system line-of-sight and improves with decreasing range. Standard deviations of the range and azimuth estimators are compared to the Cramer-Rao lower bounds. The parabolic fit method has large variance but zero bias at large deviations from the line-of-sight. The system operation is generalized from isolated obstacles to extended obstacles. >

An inertial navigation system for a mobile robot

A low-cost, solid-state inertial navigation system for robotics applications is described. Error models for the inertial sensors are generated and included in an extended Kalman filter (EKF) for estimating the position and orientation of a moving robot vehicle. A solid-state gyroscope and an accelerometer have been evaluated. Without error compensation, the error in orientation is between 5-15/spl deg//min but can be improved at least by a factor of five if an adequate error model is supplied. Similar error models have been developed for each axis of a solid-state triaxial accelerometer. Linear position estimation with accelerometers and tilt sensors is more susceptible to errors due to the double integration process involved in estimating position. WIth the system described here, the position drift rate is 1-8 cm/s, depending on the frequency of acceleration changes. The results show that with careful and detailed modeling of error sources, low cost inertial sensing systems can provide valuable position information.

Fast processing techniques for accurate ultrasonic range measurements

Four methods of range measurement for airborne ultrasonic systems - namely simple thresholding, curve-fitting, sliding-window, and correlation detection - are compared on the basis of bias error, standard deviation, total error, robustness to noise, and the difficulty/complexity of implementation. Whereas correlation detection is theoretically optimal, the other three methods can offer acceptable performance at much lower cost. Performances of all methods have been investigated as a function of target range, azimuth, and signal-to-noise ratio. Curve fitting, sliding window, and thresholding follow correlation detection in the order of decreasing complexity. Apart from correlation detection, minimum bias and total error is most consistently obtained with the curve-fitting method. On the other hand, the sliding-window method is always better than the thresholding and curve-fitting methods in terms of minimizing the standard deviation. The experimental results are in close agreement with the corresponding simulation results. Overall, the three simple and fast processing methods provide a variety of attractive compromises between measurement accuracy and system complexity. Although this paper concentrates on ultrasonic range measurement in air, the techniques described may also find application in underwater acoustics.

Human activity recognition using inertial/magnetic sensor units

This paper provides a comparative study on the different techniques of classifying human activities that are performed using bodyworn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost.

Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units

This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered.A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times.