On the Development of a Generic Multi-Sensor Fusion Framework for Robust Odometry Estimation

In this work we review the design choices, the mathematical and software engineering techniques employed in the development of the ROAMFREE sensor fusion library, a general, open-source framework for pose tracking and sensor parameter self-calibration in mobile robotics. In ROAMFREE, a comprehensive logical sensor library allows to abstract from the actual sensor hardware and processing while preserving model accuracy thanks to a rich set of calibration parameters, such as biases, gains, distortion matrices and geometric placement dimensions. The modular formulation of the sensor fusion problem, which is based on state-of-the-art factor graph inference techniques, allows to handle arbitrary number of multi-rate sensors and to adapt to virtually any kind of mobile robot platform, such as Ackerman steering vehicles, quadrotor unmanned aerial vehicles, omni-directional mobile robots. Different solvers are available to target high-rate online pose tracking tasks and offline accurate trajectory smoothing and parameter calibration. The modularity, versatility and out-of-the-box functioning of the resulting framework came at the cost of an increased complexity of the software architecture, with respect to an ad-hoc implementation of a platform dependent sensor fusion algorithm, and required careful design of abstraction layers and decoupling interfaces between solvers, state variables representations and sensor error models. However, we review how a high level, clean, C++/Python API, as long as ROS interface nodes, hide the complexity of sensor fusion tasks to the end user, making ROAMFREE an ideal choice for new, and existing, mobile robot projects

Cooperative simultaneous localization and mapping framework

This research work is a contribution to develop a framework for cooperative simultaneous localization and mapping with multiple heterogeneous mobile robots. The presented research work contributes in two aspects of a team of heterogeneous mobile robots for cooperative map building. First it provides a mathematical framework for cooperative localization and geometric features based map building. Secondly it proposes a software framework for controlling, configuring and managing a team of heterogeneous mobile robots. Since mapping and pose estimation are very closely related to each other, therefore, two novel sensor data fusion techniques are also presented, furthermore, various state of the art localization and mapping techniques and mobile robot software frameworks are discussed for an overview of the current development in this research area. The mathematical cooperative SLAM formulation probabilistically solves the problem of estimating the robots state and the environment features using Kalman filter. The software framework is an effort toward the ongoing standardization process of the cooperative mobile robotics systems. To enhance the efficiency of a cooperative mobile robot system the proposed software framework addresses various issues such as different communication protocol structure for mobile robots, different sets of sensors for mobile robots, sensor data organization from different robots, monitoring and controlling robots from a single interface. The present work can be applied to number of applications in various domains where a priori map of the environment is not available and it is not possible to use global positioning devices to find the accurate position of the mobile robot. Therefore the mobile robot(s) has to rely on building the map of its environment and using the same map to find its position and orientation relative to the environment. The exemplary areas for applying the proposed SLAM technique are Indoor environments such as warehouse management, factory floors for parts assembly line, mapping abandoned tunnels, disaster struck environment which are missing maps, under see pipeline inspection, ocean surveying, military applications, planet exploration and many others. These applications are some of many and are only limited by the imagination.Diese Forschungsarbeit ist ein Beitrag zur Entwicklung eines Framework für kooperatives SLAM mit heterogenen, mobilen Robotern. Die präsentierte Forschungsarbeit trägt in zwei Aspekten in einem Team von heterogenen, mobilen Robotern bei. Erstens stellt es einen mathematischen Framework für kooperative Lokalisierung und geometrisch basierende Kartengenerierung bereit. Zweitens schlägt es einen Softwareframework zur Steuerung, Konfiguration und Management einer Gruppe von heterogenen mobilen Robotern vor. Da Kartenerstellung und Poseschätzung miteinander stark verbunden sind, werden zwei neuartige Techniken zur Sensordatenfusion präsentiert. Weiterhin werden zum Stand der Technik verschiedene Techniken zur Lokalisierung und Kartengenerierung sowie Softwareframeworks für die mobile Robotik diskutiert um einen Überblick über die aktuelle Entwicklung in diesem Forschungsbereich zu geben. Die mathematische Formulierung des SLAM Problems löst das Problem der Roboterzustandsschätzung und der Umgebungmerkmale durch Benutzung eines Kalman filters. Der Softwareframework ist ein Beitrag zum anhaltenden Standardisierungsprozess von kooperativen, mobilen Robotern. Um die Effektivität eines kooperativen mobilen Robotersystems zu verbessern enthält der vorgeschlagene Softwareframework die Möglichkeit die Kommunikationsprotokolle flexibel zu ändern, mit verschiedenen Sensoren zu arbeiten sowie die Möglichkeit die Sensordaten verschieden zu organisieren und verschiedene Roboter von einem Interface aus zu steuern. Die präsentierte Arbeit kann in einer Vielzahl von Applikationen in verschiedenen Domänen benutzt werden, wo eine Karte der Umgebung nicht vorhanden ist und es nicht möglich ist GPS Daten zur präzisen Lokalisierung eines mobilen Roboters zu nutzen. Daher müssen die mobilen Roboter sich auf die selbsterstellte Karte verlassen und die selbe Karte zur Bestimmung von Position und Orientierung relativ zur Umgebung verwenden. Die exemplarischen Anwendungen der vorgeschlagenen SLAM Technik sind Innenraumumgebungen wie Lagermanagement, Fabrikgebäude mit Produktionsstätten, verlassene Tunnel, Katastrophengebiete ohne aktuelle Karte, Inspektion von Unterseepipelines, Ozeanvermessung, Militäranwendungen, Planetenerforschung und viele andere. Diese Anwendungen sind einige von vielen und sind nur durch die Vorstellungskraft limitiert

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Humanoid odometric localization integrating kinematic, inertial and visual information

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control

Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory

To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for pose estimation (i.e., determining the robot's rotation and position), which is the prerequisite of autonomous navigation. While the general localization algorithms have been extensively studied in research communities, there are still fundamental problems that need to be resolved for localizing skid-steering robots that change their orientation with a skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, and so on. To prevent performance reduction in robots' lifelong missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.Comment: 18 pages in tota

Automatic Calibration of Multiple Coplanar Sensors

This paper describes an algorithm for recovering the rigid 3-DOF transformation (offset and rotation) between pairs of sensors mounted rigidly in a common plane on a mobile robot. The algorithm requires only a set of sensor observations made as the robot moves along a suitable path. Our method does not require synchronized sensors; nor does it require complete metrical reconstruction of the environment or the sensor path. We show that incremental pose measurements alone are sufficient to recover sensor calibration through nonlinear least squares estimation. We use the Fisher Information Matrix to compute a Cramer-Rao lower bound (CRLB) for the resulting calibration. Applying the algorithm in practice requires a non-degenerate motion path, a principled procedure for estimating per-sensopose displacements and their covariances, a way to temporally resample asynchronous sensor data, and a way to assess the quality of the recovered calibration. We give constructive methods for each step. We demonstrate and validate the end-to-end calibration procedure for both simulated and real LIDAR and inertial data, achieving CRLBs, and corresponding calibrations, accurate to millimeters and milliradians. Source code is available from http://rvsn.csail.mit.edu/calibration

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed