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

Radius of curvature estimation and localization of targets using multiple sonar sensors

Acoustic sensors have been widely used in time-of-flight ranging systems since they are inexpensive and convenient to use. One of the most important limitations of these sensors is their low angular resolution. To improve the angular resolution and the accuracy, a novel, flexible, and adaptive three- dimensional (3-D) multi-sensor sonar system is described for estimating the radius of curvature and location of cylindrical and spherical targets. Point, line, and planar targets are included as limiting cases which are important for the characterization of typical environments. Sensitivity analysis of the curvature estimate with respect to measurement errors and certain system parameters is provided. The analysis and the simulations are verified by experiments in 2-D with specularly reflecting cylindrical and planar targets, using a real sonar system. Typical accuracies in range and azimuth are 0.18 mm and 0.1°, respectively. Accuracy of the curvature estimation depends on the target type and system parameters such as transducer separation and operating range. The adaptive configuration brings an improvement varying between 35% and 45% in the accuracy of the curvature estimate. The presented results are useful for target differentiation and tracking applications.A flexible and adaptive three-dimensional multisensor sonar system capable of estimating the location and radius of curvature of spherical and cylindrical targets is presented. The performance radius of curvature estimation is analyzed to provide information for differentiating reflectors with different radii. Results showed that the adaptive configuration improved the accuracy of the curvature estimate between 35% and 45%

clustering and pca for reconstructing two perpendicular planes using ultrasonic sensors

In this paper, the authors make use of sonar transducers to detect the corner of two orthogonal panels and they propose a strategy for accurately reconstructing the surfaces. In order to point a linear array of four sensors at the desired position, the motion of a digital motor is appropriately controlled. When the sensors are directed towards the intersection between the planes, longer times of flight are observed because of multiple reflections. All the concerned distances have to be excluded and that is why an indicator based on the output signal energy is introduced. A clustering technique allows for the partitioning of the dataset in three clusters and the indicator selects the subset containing misrepresented information. The remaining distances are corrected so as to take into consideration the directivity and they permit the plotting of two sets of points in a three-dimensional space. In order to leave out the outliers, each set is filtered by means of a confidence ellipsoid which is defined by the Principal Component Analysis (PCA). The best-fit planes are obtained based on the principal directions and the variances. Experimental tests and results are shown demonstrating the effectiveness of this new approach

Underwater object localization using a biomimetic binaural sonar

Thesis (S.M. in Oceanographic Engineering)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering; and the Woods Hole Oceanographic Institution), 1999.Includes bibliographical references (leaves 85-89).by Qiang Wang.S.M.in Oceanographic Engineerin

Radius of curvature and location estimation of cylindrical objects with sonar using a multi-sensor configuration

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 130-133.Despite their limitations, sonar sensors are very popular in time-of-flight measuring systems since they are inexpensive and convenient. One of the most important limitations of sonar is its low angular resolution. An adjustable multi-sonar configuration consisting of three transmitter/receiver ultrasonic transducers is used to improve the resolution. The radius of curvature estimation of cylindrical objects is accomplished with this configuration. Two different ways of rotating the transducers are considered. First, the sensors are rotated around their joints. Second, the sensors are rotated around their centers. Also, two methods of tirne-of-flight estimation are implemented which are thresholding and curve-fitting. Sensitivity analysis of the radius of curvature with respect to some important parameters is made. The bias-variance combinations of both estimators are compared to the Cramer-Rao lower bound. Theory and simulations are verified by experimental data from real sonar systems. Data is smoothed by extended Kalman filtering. Rotating around the center works better than rotating around the joint. Curve-fitting method is shown to be better than thresholding method both in the absence and presence of noise. The best results are obtained w'hen the sensors are rotated around their centers and the curve-fitting method is used to estimate the time- of-flight. There is about 30% improvement in the absence of noise and 50% improvement in the presence of noise.Sekmen, Ali ŞafakM.S

Autonomous navigation for guide following in crowded indoor environments

The requirements for assisted living are rapidly changing as the number of elderly patients over the age of 60 continues to increase. This rise places a high level of stress on nurse practitioners who must care for more patients than they are capable. As this trend is expected to continue, new technology will be required to help care for patients. Mobile robots present an opportunity to help alleviate the stress on nurse practitioners by monitoring and performing remedial tasks for elderly patients. In order to produce mobile robots with the ability to perform these tasks, however, many challenges must be overcome. The hospital environment requires a high level of safety to prevent patient injury. Any facility that uses mobile robots, therefore, must be able to ensure that no harm will come to patients whilst in a care environment. This requires the robot to build a high level of understanding about the environment and the people with close proximity to the robot. Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders. 3D time-of-flight sensors have recently been introduced and provide dense 3D point clouds of the environment at real-time frame rates. This provides mobile robots with previously unavailable dense information in real-time. I investigate the use of time-of-flight cameras for mobile robot navigation in crowded environments in this thesis. A unified framework to allow the robot to follow a guide through an indoor environment safely and efficiently is presented. Each component of the framework is analyzed in detail, with real-world scenarios illustrating its practical use. Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems that must be overcome to receive consistent and accurate data. I propose a novel and practical probabilistic framework to overcome many of the inherent problems in this thesis. The framework fuses multiple depth maps with color information forming a reliable and consistent view of the world. In order for the robot to interact with the environment, contextual information is required. To this end, I propose a region-growing segmentation algorithm to group points based on surface characteristics, surface normal and surface curvature. The segmentation process creates a distinct set of surfaces, however, only a limited amount of contextual information is available to allow for interaction. Therefore, a novel classifier is proposed using spherical harmonics to differentiate people from all other objects. The added ability to identify people allows the robot to find potential candidates to follow. However, for safe navigation, the robot must continuously track all visible objects to obtain positional and velocity information. A multi-object tracking system is investigated to track visible objects reliably using multiple cues, shape and color. The tracking system allows the robot to react to the dynamic nature of people by building an estimate of the motion flow. This flow provides the robot with the necessary information to determine where and at what speeds it is safe to drive. In addition, a novel search strategy is proposed to allow the robot to recover a guide who has left the field-of-view. To achieve this, a search map is constructed with areas of the environment ranked according to how likely they are to reveal the guide’s true location. Then, the robot can approach the most likely search area to recover the guide. Finally, all components presented are joined to follow a guide through an indoor environment. The results achieved demonstrate the efficacy of the proposed components

Anomaly detection & object classification using multi-spectral LiDAR and sonar

In this thesis, we present the theory of high-dimensional signal approximation of multifrequency signals. We also present both linear and non-linear compressive sensing (CS) algorithms that generate encoded representations of time-correlated single photon counting (TCSPC) light detection and ranging (LiDAR) data, side-scan sonar (SSS) and synthetic aperture sonar (SAS). The main contributions of this thesis are summarised as follows: 1. Research is carried out studying full-waveform (FW) LiDARs, in particular, the TCSPC data, capture, storage and processing. 2. FW-LiDARs are capable of capturing large quantities of photon-counting data in real-time. However, the real-time processing of the raw LiDAR waveforms hasn’t been widely exploited. This thesis answers some of the fundamental questions: • can semantic information be extracted and encoded from raw multi-spectral FW-LiDAR signals? • can these encoded representations then be used for object segmentation and classification? 3. Research is carried out into signal approximation and compressive sensing techniques, its limitations and the application domains. 4. Research is also carried out in 3D point cloud processing, combining geometric features with material spectra (spectral-depth representation), for object segmentation and classification. 5. Extensive experiments have been carried out with publicly available datasets, e.g. the Washington RGB Image and Depth (RGB-D) dataset [108], YaleB face dataset1 [110], real-world multi-frequency aerial laser scans (ALS)2 and an underwater multifrequency (16 wavelengths) TCSPC dataset collected using custom-build targets especially for this thesis. 6. The multi-spectral measurements were made underwater on targets with different shapes and materials. A novel spectral-depth representation is presented with strong discrimination characteristics on target signatures. Several custom-made and realistically scaled exemplars with known and unknown targets have been investigated using a multi-spectral single photon counting LiDAR system. 7. In this work, we also present a new approach to peak modelling and classification for waveform enabled LiDAR systems. Not all existing approaches perform peak modelling and classification simultaneously in real-time. This was tested on both simulated waveform enabled LiDAR data and real ALS data2 . This PhD also led to an industrial secondment at Carbomap, Edinburgh, where some of the waveform modelling algorithms were implemented in C++ and CUDA for Nvidia TX1 boards for real-time performance. 1http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ 2This dataset was captured in collaboration with Carbomap Ltd. Edinburgh, UK. The data was collected during one of the trials in Austria using commercial-off-the-shelf (COTS) sensors

A model for the simulation of sidescan sonar

This thesis presents the development of a computer model for the simulation of the sidescan sonar process. The motivation for the development of this model is the creation of a unique and powerful visualisation tool to improve understanding and interpretation of the sidescan sonar process and the images created by it. Existing models tend to generate graphical or numerical results, but this model produces synthetic sidescan sonar images as the output. This permits the direct visualisation of the influence of individual parameters and features of the sonar process on the sidescan images. The model considers the main deterministic aspects of the underlying physical processes which result in the generation of sidescan sonar images. These include the propagation of the transmitted pulse of acoustic energy through the water column to its subsequent interaction and scattering from the rough seafloor. The directivity and motion characteristics of the sonar transducer are also incorporated. The thesis documents the development of the model to include each of these phenomena and their subsequent effect on the sidescan sonar images. Finally, techniques are presented for the investigation and verification of the synthetic sidescan images produced by the model.Defence Research Agenc