Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

ATLAS: a framework for large scale automated mapping and localization

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 203-207).This thesis describes a scalable robotic navigation system that builds a map of the robot's environment on the fly. This problem is also known as Simultaneous Localization and Mapping (SLAM). The SLAM problem has as inputs the control of the robot's motion and sensor measurements to features in the environment. The desired output is the path traversed by the robot (localization) and a representation of the sensed environment (mapping). The principal contribution of this thesis is the introduction of a framework, termed Atlas, that alleviates the computational restrictions of previous approaches to SLAM when mapping extended environments. The Atlas framework partitions the SLAM problem into a graph of submaps, each with its own coordinate system. Furthermore, the framework facilitates the modularity of sensors, map representations, and local navigation algorithms by encapsulating the implementation specific algorithms into an abstracted module. The challenge of loop closing is handled with a module that matches submaps and a verification procedure that trades latency in loop closing with a lower chance of incorrect loop detections inherent with symmetric environments. The framework is demonstrated with several datasets that map large indoor and urban outdoor environments using a variety of sensors: a laser scanner, sonar rangers, and omni-directional video.by Michael Carsten Bosse.Ph.D

Vision-based methods for state estimation and control of robotic systems with application to mobile and surgical robots

For autonomous systems that need to perceive the surrounding environment for the accomplishment of a given task, vision is a highly informative exteroceptive sensory source. When gathering information from the available sensors, in fact, the richness of visual data allows to provide a complete description of the environment, collecting geometrical and semantic information (e.g., object pose, distances, shapes, colors, lights). The huge amount of collected data allows to consider both methods exploiting the totality of the data (dense approaches), or a reduced set obtained from feature extraction procedures (sparse approaches). This manuscript presents dense and sparse vision-based methods for control and sensing of robotic systems. First, a safe navigation scheme for mobile robots, moving in unknown environments populated by obstacles, is presented. For this task, dense visual information is used to perceive the environment (i.e., detect ground plane and obstacles) and, in combination with other sensory sources, provide an estimation of the robot motion with a linear observer. On the other hand, sparse visual data are extrapolated in terms of geometric primitives, in order to implement a visual servoing control scheme satisfying proper navigation behaviours. This controller relies on visual estimated information and is designed in order to guarantee safety during navigation. In addition, redundant structures are taken into account to re-arrange the internal configuration of the robot and reduce its encumbrance when the workspace is highly cluttered. Vision-based estimation methods are relevant also in other contexts. In the field of surgical robotics, having reliable data about unmeasurable quantities is of great importance and critical at the same time. In this manuscript, we present a Kalman-based observer to estimate the 3D pose of a suturing needle held by a surgical manipulator for robot-assisted suturing. The method exploits images acquired by the endoscope of the robot platform to extrapolate relevant geometrical information and get projected measurements of the tool pose. This method has also been validated with a novel simulator designed for the da Vinci robotic platform, with the purpose to ease interfacing and employment in ideal conditions for testing and validation. The Kalman-based observers mentioned above are classical passive estimators, whose system inputs used to produce the proper estimation are theoretically arbitrary. This does not provide any possibility to actively adapt input trajectories in order to optimize specific requirements on the performance of the estimation. For this purpose, active estimation paradigm is introduced and some related strategies are presented. More specifically, a novel active sensing algorithm employing visual dense information is described for a typical Structure-from-Motion (SfM) problem. The algorithm generates an optimal estimation of a scene observed by a moving camera, while minimizing the maximum uncertainty of the estimation. This approach can be applied to any robotic platforms and has been validated with a manipulator arm equipped with a monocular camera

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

RUUMBA: a Range-only, Unscented, Undelayed, Mobile Beacon-Assisted framework for WSN discovery and localization

This thesis concerns the problem of localizing the nodes of a WSN using only RSSI range measurements from an autonomous mobile robot. Framing it as a SLAM problem, state of the art techniques such as the Unscented Kalman Filter and GMM undelayed initialization are joined in a single context. Moreover, different path planning strategies for optimal information/energy expenditure ratio are developed and compared simulationally

Resilient visual perception for multiagent systems

There has been an increasing interest in visual sensors and vision-based solutions for single and multi-robot systems. Vision-based sensors, e.g., traditional RGB cameras, grant rich semantic information and accurate directional measurements at a relatively low cost; however, such sensors have two major drawbacks. They do not generally provide reliable depth estimates, and typically have a limited field of view. These limitations considerably increase the complexity of controlling multiagent systems. This thesis studies some of the underlying problems in vision-based multiagent control and mapping. The first contribution of this thesis is a method for restoring bearing rigidity in non-rigid networks of robots. We introduce means to determine which bearing measurements can improve bearing rigidity in non-rigid graphs and provide a greedy algorithm that restores rigidity in 2D with a minimum number of added edges. The focus of the second part is on the formation control problem using only bearing measurements. We address the control problem for consensus and formation control through non-smooth Lyapunov functions and differential inclusion. We provide a stability analysis for undirected graphs and investigate the derived controllers for directed graphs. We also introduce a newer notion of bearing persistence for pure bearing-based control in directed graphs. The third part is concerned with the bearing-only visual homing problem with a limited field of view sensor. In essence, this problem is a special case of the formation control problem where there is a single moving agent with fixed neighbors. We introduce a navigational vector field composed of two orthogonal vector fields that converges to the goal position and does not violate the field of view constraints. Our method does not require the landmarks' locations and is robust to the landmarks' tracking loss. The last part of this dissertation considers outlier detection in pose graphs for Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) problems. We propose a method for detecting incorrect orientation measurements before pose graph optimization by checking their geometric consistency in cycles. We use Expectation-Maximization to fine-tune the noise's distribution parameters and propose a new approximate graph inference procedure specifically designed to take advantage of evidence on cycles with better performance than standard approaches. These works will help enable multi-robot systems to overcome visual sensors' limitations in collaborative tasks such as navigation and mapping