Cost-effective robot for steep slope crops monitoring

This project aims to develop a low cost, simple and robust robot able to autonomously monitorcrops using simple sensors. It will be required do develop robotic sub-systems and integrate them with pre-selected mechanical components, electrical interfaces and robot systems (localization, navigation and perception) using ROS, for wine making regions and maize fields

External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Recent Developments in Monocular SLAM within the HRI Framework

This chapter describes an approach to improve the feature initialization process in the delayed inverse-depth feature initialization monocular Simultaneous Localisation and Mapping (SLAM), using data provided by a robot’s camera plus an additional monocular sensor deployed in the headwear of the human component in a human-robot collaborative exploratory team. The robot and the human deploy a set of sensors that once combined provides the data required to localize the secondary camera worn by the human. The approach and its implementation are described along with experimental results demonstrating its performance. A discussion on the usual sensors within the robotics field, especially in SLAM, provides background to the advantages and capabilities of the system implemented in this research

Shaped-based IMU/Camera Tightly Coupled Object-level SLAM using Rao-Blackwellized Particle Filtering

Simultaneous Localization and Mapping (SLAM) is a decades-old problem. The classical solution to this problem utilizes entities such as feature points that cannot facilitate the interactions between a robot and its environment (e.g., grabbing objects). Recent advances in deep learning have paved the way to accurately detect objects in the image under various illumination conditions and occlusions. This led to the emergence of object-level solutions to the SLAM problem. Current object-level methods depend on an initial solution using classical approaches and assume that errors are Gaussian. This research develops a standalone solution to object-level SLAM that integrates the data from a monocular camera and an IMU (available in low-end devices) using Rao Blackwellized Particle Filter (RBPF). RBPF does not assume Gaussian distribution for the error; thus, it can handle a variety of scenarios (such as when a symmetrical object with pose ambiguities is encountered). The developed method utilizes shape instead of texture; therefore, texture-less objects can be incorporated into the solution. In the particle weighing process, a new method is developed that utilizes the Intersection over the Union (IoU) area of the observed and projected boundaries of the object that does not require point-to-point correspondence. Thus, it is not prone to false data correspondences. Landmark initialization is another important challenge for object-level SLAM. In the state-of-the-art delayed initialization, the trajectory estimation only relies on the motion model provided by IMU mechanization (during the initialization), leading to large errors. In this thesis, two novel undelayed initializations are developed. One relies only on a monocular camera and IMU, and the other utilizes an ultrasonic rangefinder as well. The developed object-level SLAM is tested using wheeled robots and handheld devices, and an error (in the position) of 4.1 to 13.1 cm (0.005 to 0.028 of the total path length) has been obtained through extensive experiments using only a single object. These experiments are conducted in different indoor environments under different conditions (e.g. illumination). Further, it is shown that undelayed initialization using an ultrasonic sensor can reduce the algorithm's runtime by half

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

A multisensor SLAM for dense maps of large scale environments under poor lighting conditions

This thesis describes the development and implementation of a multisensor large scale autonomous mapping system for surveying tasks in underground mines. The hazardous nature of the underground mining industry has resulted in a push towards autonomous solutions to the most dangerous operations, including surveying tasks. Many existing autonomous mapping techniques rely on approaches to the Simultaneous Localization and Mapping (SLAM) problem which are not suited to the extreme characteristics of active underground mining environments. Our proposed multisensor system has been designed from the outset to address the unique challenges associated with underground SLAM. The robustness, self-containment and portability of the system maximize the potential applications.The multisensor mapping solution proposed as a result of this work is based on a fusion of omnidirectional bearing-only vision-based localization and 3D laser point cloud registration. By combining these two SLAM techniques it is possible to achieve some of the advantages of both approaches – the real-time attributes of vision-based SLAM and the dense, high precision maps obtained through 3D lasers. The result is a viable autonomous mapping solution suitable for application in challenging underground mining environments.A further improvement to the robustness of the proposed multisensor SLAM system is a consequence of incorporating colour information into vision-based localization. Underground mining environments are often dominated by dynamic sources of illumination which can cause inconsistent feature motion during localization. Colour information is utilized to identify and remove features resulting from illumination artefacts and to improve the monochrome based feature matching between frames.Finally, the proposed multisensor mapping system is implemented and evaluated in both above ground and underground scenarios. The resulting large scale maps contained a maximum offset error of ±30mm for mapping tasks with lengths over 100m

A Survey on Odometry for Autonomous Navigation Systems

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed