CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

Comparing LiDAR and IMU-based SLAM approaches for 3D robotic mapping

In this paper, we propose a comparison of open-source LiDAR and Inertial Measurement Unit (IMU)-based Simultaneous Localization and Mapping (SLAM) approaches for 3D robotic mapping. The analyzed algorithms are often exploited in mobile robotics for autonomous navigation but have not been evaluated in terms of 3D reconstruction yet. Experimental tests are carried out using two different autonomous mobile platforms in three test cases, comprising both indoor and outdoor scenarios. The 3D models obtained with the different SLAM algorithms are then compared in terms of density, accuracy, and noise of the point clouds to analyze the performance of the evaluated approaches. The experimental results indicate the SLAM methods that are more suitable for 3D mapping in terms of the quality of the reconstruction and highlight the feasibility of mobile robotics in the field of autonomous mapping

다중 로봇 SLAM을 위한 상관관계 기반 지도병합 기술

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이범희.Multi-robot simultaneous localization and mapping (SLAM) is an advanced technique used by multiple robots and autonomous vehicles to build up a collective map within an unknown environment, or to update a collective map within a known environment, while at the same time keeping track of their current location. The collective map is obtained by merging individual maps built by different multiple robots exploring the environment. When robots do not know their initial poses one another, the problem of map merging becomes challenging because the robots have different coordinate systems. If robot-to-robot measurements are not available, the problem of map merging becomes more challenging because the map transformation matrix (MTM) among robots cannot be computed directly. This dissertation presents novel map merging techniques based on the analysis of the correlation among the individual maps, which do not need the knowledge of the relative initial poses of robots and the robot-to-robot measurements. After the cross-correlation function among the spectrometric or tomographic information extracted from the individual maps is generated, the MTM is computed by taking the rotation angle and the translation amounts corresponding to the maximum cross-correlation values. The correlation-based map merging techniques with spectral information presented in this dissertation are the extensions of a conventional map merging technique. One extension is spectrum-based feature map merging (SFMM), which extracts the spectral information of feature maps from virtual supporting lines and computes the MTM by matching the extracted spectral information. The other extension is enhanced-spectrum-based map merging (ESMM), which enhances grid maps using the locations of visual objects and computes the MTM by matching the spectral information extracted from the enhanced grid maps. The two extensions overcome successfully the limitation of the conventional map merging technique. The correlation-based map merging technique with tomographic information is a new map merging technique, which is named tomographic map merging (TMM). Since tomographic analysis can provide more detailed information on grid maps according to rotation and translation than spectral analysis, the more accurate MTM can be computed by matching the tomographic information. The TMM was tested on various pairs of partial maps from real experiments in indoor and outdoor environments. The improved accuracy was verified by showing smaller map merging errors than the conventional map merging technique and several existing map merging techniques.Chapter 1 Introduction 1.1 Background and motivation 1.2 Related works 1.3 Contributions 1.4 Organization Chapter 2 Multi-Robot SLAM and Map Merging 2.1 SLAM using Particle Filters 2.2 Multi-Robot SLAM (MR-SLAM) 2.2.1 MR-SLAM with Known Initial Correspondences 2.2.2 MR-SLAM with Unknown Initial Correspondences 2.3 Map Merging Chapter 3 Map Merging based on Spectral Correlation 3.1 Spectrum-based Map Merging (SMM) 3.2 Spectrum-based Feature Map Merging (SFMM) 3.2.1 Overview of the SFMM 3.2.2 Problem Formulation for the SFMM 3.2.3 Virtual Supporting Lines (VSLs) 3.2.4 Estimation of Map Rotation with Hough Spectra 3.2.5 Rasterization of Updated Feature Maps with VSLs 3.2.6 Estimation of Map Displacements 3.3 Enhanced-Spectrum-based Map Merging (ESMM) 3.3.1 Overview of the ESMM 3.3.2 Problem Formulation for the ESMM 3.3.3 Preprocessing – Map Thinning 3.3.4 Map Enhancement 3.3.5 Estimation of Map Rotation 3.3.6 Estimation of Map Translations Chapter 4 Map Merging based on Tomographic Correlation 4.1 Overview of the TMM 4.2 Problem Formulation for the TMM 4.3 Extraction of Sinograms by the Radon Transform 4.4 Estimation of a Rotation Angle 4.5 Estimation of X-Y Translations Chapter 5 Experiments 5.1 Experimental Results of the SFMM 5.2 Experimental Results of the ESMM 5.2.1 Results in a Parking Area 5.2.2 Results in a Building Roof 5.3 Experimental Results of the TMM 5.3.1 Results in Indoor Environments 5.3.2 Results in Outdoor Environments 5.3.3 Results with a Public Dataset 5.3.4 Results of Merging More Maps 5.4 Comparison among the Proposed Techniques 5.5 Discussion Chapter 6 Conclusions BibliographyDocto

Stochastic filtering on mobile devices in complex dynamic environments

Gathering information, especially about the immediately surrounding world, is a central aspect of any smart device, whether it is a robot, a partially autonomous vehicle, or a mobile handheld device. The consequential use of electrical sensors always implies the need to filter the imperfect sensor data output in order to gain reliable information. While the challenge of perception and cognition in machines is not a new one, new technology constantly opens up new possibilities and challenges. This is stressed further by the advent of cheap sensor technology and the possibility to use a multitude of small sensors, with the simultaneous constraint of limited resources on mobile, battery-powered computing devices. In this work, stochastic methods are used to filter sensor data, which is gathered by mobile devices, to model the devices' location and eventually also relevant parts of their dynamic environment. This is done with a focus on online algorithms and computation on these mobile devices themselves, which implies limited available processing power and the necessity for computational efficiency. This dissertation's purpose is to impart a better understanding about the conception and design of stochastic filtering solutions, to propose localization algorithms beyond the current state of the art, and to show the use of simultaneous localization and mapping algorithms in the context of cooperatively estimating the surrounding world of a team of robots in a fast changing, dynamic environment. To achieve these goals, the concepts are depicted in multiple application scenarios, design choices and their implications systematically cover all aspects of sensing and estimation, and the proposed systems are evaluated in real-world experiments on humanoid robots and other mobile devices