Multiple Autonomous Systems in Underwater Mine Countermeasures Mission Using Various Information Fusion as Navigation Aid

Autonomous bottom mine neutralization systems have a challenging task of mine reacquisition and navigation in the demanding underwater environment. Even after mine reacquisition, the neutralization payload has to be autonomously deployed near the mine, and before any action the verification (classification) of the existence of a mine has to be determined. The mine intervention vehicle can be an expendable (self-destroyed during the mine neutralization) or a vehicle that deploys the neutralization payload and it is retrieved at the end of the mission. Currently the systems developed by the research community are capable of remotely navigating a mine intervention underwater vehicle in the vicinity of the mine by using remote sonar aided navigation from a master vehicle. However, the task of successfully navigating the vehicle that carries the neutralization payload near the bottom and around the mine remains a challenge due to sea bottom clutter and the target signature interfering with the sonar detection. We seek a solution by introducing navigation via visual processing near the mine location. Using an onboard camera the relative distance to the mine-like object can be estimated. This will improve the overall vehicle navigation and rate of successful payload delivery close to the mine. The paper will present the current navigation system of the mine intervention underwater vehicle and the newly developed visual processing for relative position estimation

Error Analysis in a Stereo Vision-Based Pedestrian Detection Sensor for Collision Avoidance Applications

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance

2D position system for a mobile robot in unstructured environments

Nowadays, several sensors and mechanisms are available to estimate a mobile robot trajectory and location with respect to its surroundings. Usually absolute positioning mechanisms are the most accurate, but they also are the most expensive ones, and require pre installed equipment in the environment. Therefore, a system capable of measuring its motion and location within the environment (relative positioning) has been a research goal since the beginning of autonomous vehicles. With the increasing of the computational performance, computer vision has become faster and, therefore, became possible to incorporate it in a mobile robot. In visual odometry feature based approaches, the model estimation requires absence of feature association outliers for an accurate motion. Outliers rejection is a delicate process considering there is always a trade-off between speed and reliability of the system. This dissertation proposes an indoor 2D position system using Visual Odometry. The mobile robot has a camera pointed to the ceiling, for image analysis. As requirements, the ceiling and the oor (where the robot moves) must be planes. In the literature, RANSAC is a widely used method for outlier rejection. However, it might be slow in critical circumstances. Therefore, it is proposed a new algorithm that accelerates RANSAC, maintaining its reliability. The algorithm, called FMBF, consists on comparing image texture patterns between pictures, preserving the most similar ones. There are several types of comparisons, with different computational cost and reliability. FMBF manages those comparisons in order to optimize the trade-off between speed and reliability

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

Détection et localisation des panneaux routiers

Les panneaux routiers -- Obtention et gestion de la position des panneaux -- Description du système d'inspection et présentation des données -- Le système d'inspection -- Segmentation basée sur la couleur -- Détection des panneaux grâce à leur forme -- Solution proposée pour la détection des panneaux -- Pré-reconnaissance de forme -- Localisation des panneaux dans l'image -- Suivi et localisation des panneaux -- Utilisation de la redonnance temporelle -- Suivi des panneaux -- Localisation des panneaux à posteriori -- Simulation de l'inspection d'une portion de route

Novel Camera Architectures for Localization and Mapping on Intelligent Mobile Platforms

Self-localization and environment mapping play a very important role in many robotics application such as autonomous driving and mixed reality consumer products. Although the most powerful solutions rely on a multitude of sensors including lidars and camera, the community maintains a high interest in developing cost-effective, purely vision-based localization and mapping approaches. The core problem of standard vision-only solutions is accuracy and robustness, especially in challenging visual conditions. The thesis aims to introduce new solutions to localization and mapping problems on intelligent mobile devices by taking advantages of novel camera architectures. The thesis investigates on using surround-view multi-camera systems, which combine the benefits of omni-directional measurements with a sufficient baseline for producing measurements in metric scale, and event cameras, that perform well under challenging illumination conditions and have high temporal resolutions. The thesis starts by looking into the motion estimation framework with multi-perspective camera systems. The framework could be divided into two sub-parts, a front-end module that initializes motion and estimates absolute pose after bootstrapping, and a back-end module that refines the estimate over a larger-scale sequence. First, the thesis proposes a complete real-time pipeline for visual odometry with non-overlapping, multi-perspective camera systems, and in particular presents a solution to the scale initialization problem, in order to solve the unobservability of metric scale under degenerate cases with such systems. Second, the thesis focuses on the further improvement of front-end relative pose estimation for vehicle-mounted surround-view multi-camera systems. It presents a new, reliable solution able to handle all kinds of relative displacements in the plane despite the possibly non-holonomic characteristics, and furthermore introduces a novel two-view optimization scheme which minimizes a geometrically relevant error without relying on 3D points related optimization variables. Third, the thesis explores the continues-time parametrization for exact modelling of non-holonomic ground vehicle trajectories in the back-end optimization of visual SLAM pipeline. It demonstrates the use of B-splines for an exact imposition of smooth, non-holonomic trajectories inside the 6 DoF bundle adjustment, and show that a significant improvement in robustness and accuracy in degrading visual conditions can be achieved. In order to deal with challenges in scenarios with high dynamics, low texture distinctiveness, or challenging illumination conditions, the thesis focuses on the solution to localization and mapping problem on Autonomous Ground Vehicle(AGV) using event cameras. Inspired by the time-continuous parametrizations of image warping functions introduced by previous works, the thesis proposes two new algorithms to tackle several motion estimation problems by performing contrast maximization approach. It firstly looks at the fronto-parallel motion estimation of an event camera, in stark contrast to the prior art, a globally optimal solution to this motion estimation problem is derived by using a branch-and-bound optimization scheme. Then, the thesis introduces a new solution to handle the localization and mapping problem of single event camera by continuous ray warping and volumetric contrast maximization, which can perform joint optimization over motion and structure for cameras exerting both translational and rotational displacements in an arbitrarily structured environment. The present thesis thus makes important contributions on both front-end and back-end of SLAM pipelines based on novel, promising camera architectures

Error modelling in stereo navigation

Computer Science Departmen