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

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Microdrone-Based Indoor Mapping with Graph SLAM

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm

INVESTIGATION OF THE UNDERGROUND BUILDING HERITAGE AND THE MECHANISM OF WATER FLOWING IN QANĀTS IN PALERMO THROUGH INNOVATIVE SURVEYING TECHNIQUES

The valorisation, protection and preservation policies for the underground building heritage are often difficult to implement due to an inadequate knowledge of hypogeal constructions. The complex and widespread underground structures of the vast “Cavo” Heritage (“horizontal wells”, “shelf wells” or “well tunnels”), so called qanāts, hidden underground and built over the centuries in Palermo, representing an evocative testimony to the history of water culture in the ancient city. Through the historical and constructive analyses and the implementation and development of measurement and 3D representation and visualization, first actions have been carried out. The paper will present the first results of the restoration project and the path of re-introduction in the fruition network of the qanāt “Gesuitico alto”, developed also in the field of “iHeritage. Mediterranean Platform for UNESCO Cultural Heritage” project, financed by ENI CBC MED Programme 2014-2020. The paper presents an experimentation of a procedural workflow of data acquisition, analysis and subsequent 3D virtual navigation of hypogeal environments. The methodology used is the SLAM with a last generation WMLS. The platform of virtual reality visualization, within UnReal Engine, allows the user to immerse and navigate in the anthropic environment by engaging it with a set of infographics that highlight the virtual visit

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others. Still, most approaches typically address visual tasks in isolation, resulting in overspecialized models which achieve strong performances in specific applications but work poorly in other (often related) tasks. This is clearly sub-optimal for a robot which is often required to perform simultaneously multiple visual recognition tasks in order to properly act and interact with the environment. This problem is exacerbated by the limited computational and memory resources typically available onboard to a robotic platform. The problem of learning flexible models which can handle multiple tasks in a lightweight manner has recently gained attention in the computer vision community and benchmarks supporting this research have been proposed. In this work we study this problem in the robot vision context, proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art algorithms in this novel challenging scenario. We also define a new evaluation protocol, better suited to the robot vision setting. Results shed light on the strengths and weaknesses of existing approaches and on open issues, suggesting directions for future research.Comment: This work has been submitted to IROS/RAL 201

PRLS-INVES: A General Experimental Investigation Strategy for High Accuracy and Precision in Passive RFID Location Systems

Due to cost-effectiveness and easy-deployment, radio-frequency identification (RFID) location systems are widely utilized into many industrial fields, particularly in the emerging environment of the Internet of Things (IoT). High accuracy and precision are key demands for these location systems. Numerous studies have attempted to improve localization accuracy and precision using either dedicated RFID infrastructures or advanced localization algorithms. But these effects mostly consider utilization of novel RFID localization solutions rather than optimization of this utilization. Practical use of these solutions in industrial applications leads to increased cost and deployment difficulty of RFID system. This paper attempts to investigate how accuracy and precision in passive RFID location systems (PRLS) are impacted by infrastructures and localization algorithms. A general experimental-based investigation strategy, PRLS-INVES, is designed for analyzing and evaluating the factors that impact the performance of a passive RFID location system. Through a case study on passive high frequency (HF) RFID location systems with this strategy, it is discovered that: 1) the RFID infrastructure is the primary factor determining the localization capability of an RFID location system and 2) localization algorithm can improve accuracy and precision, but is limited by the primary factor. A discussion on how to efficiently improve localization accuracy and precision in passive HF RFID location systems is given

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment