Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments. We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd

Long-Term Simultaneous Localization and Mapping in Dynamic Environments.

One of the core competencies required for autonomous mobile robotics is the ability to use sensors to perceive the environment. From this noisy sensor data, the robot must build a representation of the environment and localize itself within this representation. This process, known as simultaneous localization and mapping (SLAM), is a prerequisite for almost all higher-level autonomous behavior in mobile robotics. By associating the robot's sensory observations as it moves through the environment, and by observing the robot's ego-motion through proprioceptive sensors, constraints are placed on the trajectory of the robot and the configuration of the environment. This results in a probabilistic optimization problem to find the most likely robot trajectory and environment configuration given all of the robot's previous sensory experience. SLAM has been well studied under the assumptions that the robot operates for a relatively short time period and that the environment is essentially static during operation. However, performing SLAM over long time periods while modeling the dynamic changes in the environment remains a challenge. The goal of this thesis is to extend the capabilities of SLAM to enable long-term autonomous operation in dynamic environments. The contribution of this thesis has three main components: First, we propose a framework for controlling the computational complexity of the SLAM optimization problem so that it does not grow unbounded with exploration time. Second, we present a method to learn visual feature descriptors that are more robust to changes in lighting, allowing for improved data association in dynamic environments. Finally, we use the proposed tools in SLAM systems that explicitly models the dynamics of the environment in the map by representing each location as a set of example views that capture how the location changes with time. We experimentally demonstrate that the proposed methods enable long-term SLAM in dynamic environments using a large, real-world vision and LIDAR dataset collected over the course of more than a year. This dataset captures a wide variety of dynamics: from short-term scene changes including moving people, cars, changing lighting, and weather conditions; to long-term dynamics including seasonal conditions and structural changes caused by construction.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111538/1/carlevar_1.pd

Topological local-metric framework for mobile robots navigation: a long term perspective

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously

Visual Place Recognition: A Tutorial

Localization is an essential capability for mobile robots. A rapidly growing field of research in this area is Visual Place Recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images. This present work is the first tutorial paper on visual place recognition. It unifies the terminology of VPR and complements prior research in two important directions: 1) It provides a systematic introduction for newcomers to the field, covering topics such as the formulation of the VPR problem, a general-purpose algorithmic pipeline, an evaluation methodology for VPR approaches, and the major challenges for VPR and how they may be addressed. 2) As a contribution for researchers acquainted with the VPR problem, it examines the intricacies of different VPR problem types regarding input, data processing, and output. The tutorial also discusses the subtleties behind the evaluation of VPR algorithms, e.g., the evaluation of a VPR system that has to find all matching database images per query, as opposed to just a single match. Practical code examples in Python illustrate to prospective practitioners and researchers how VPR is implemented and evaluated.Comment: IEEE Robotics & Automation Magazine (RAM

Sensor fusion for flexible human-portable building-scale mapping

This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the system's ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment.Lincoln LaboratoryUnited States. Air Force (Contract FA8721-05-C-0002)United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020

Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning

Autonomous agents in any environment require accurate and reliable position and motion estimation to complete their required tasks. Many different sensor modalities have been utilized for this task such as GPS, ultra-wide band, visual simultaneous localization and mapping (SLAM), and light detection and ranging (LiDAR) SLAM. Many of the traditional positioning systems do not take advantage of the recent advances in the machine learning field. In this work, an omnidirectional camera position estimation system relying primarily on a learned model is presented. The positioning system benefits from the wide field of view provided by an omnidirectional camera. Recent developments in the self-supervised learning field for generating useful features from unlabeled data are also assessed. A novel radial patch pretext task for omnidirectional images is presented in this work. The resulting implementation will be a robot localization and tracking algorithm that can be adapted to a variety of environments such as warehouses and college campuses. Further experiments with additional types of sensors including 3D LiDAR, 60 GHz wireless, and Ultra-Wideband localization systems utilizing machine learning are also explored. A fused learned localization model utilizing multiple sensor modalities is evaluated in comparison to individual sensor models

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing