Development of a ground robot for indoor SLAM using Low‐Cost LiDAR and remote LabVIEW HMI

The simultaneous localization and mapping problem (SLAM) is crucial to autonomous navigation and robot mapping. The main purpose of this thesis is to develop a ground robot that implements SLAM to test the performance of the low‐cost RPLiDAR A1M8 by DFRobot. The HectorSLAM package, available in ROS was used with a Raspberry Pi to implement SLAM and build maps. These maps are sent to a remote desktop via TCP/IP communication to be displayed on a LabVIEW HMI where the user can also control robot. The LabVIEW HMI and the project in its entirety is intended to be as easy to use as possible to the layman, with many processes being automated to make this possible. The quality of the maps created by HectorSLAM and the RPLiDAR were evaluated both qualitatively and quanitatively to determine how useful the low‐cost LiDAR can be for this application. It is hoped that the apparatus developed in this project will be used with drones in the future for 3D mapping

Novel point-to-point scan matching algorithm based on cross-correlation

The localization of mobile robots in outdoor and indoor environments is a complex issue. Many sophisticated approaches, based on various types of sensory inputs and different computational concepts, are used to accomplish this task. However, many of the most efficient methods for mobile robot localization suffer from high computational costs and/or the need for high resolution sensory inputs. Scan cross-correlation is a traditional approach that can be, in special cases, used to match temporally aligned scans of robot environment. This work proposes a set of novel modifications to the cross-correlation method that extend its capability beyond these special cases to general scan matching and mitigate its computational costs so that it is usable in practical settings. The properties and validity of the proposed approach are in this study illustrated on a number of computational experiments.Web of Scienceart. ID 646394

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

Development of an adaptive navigation system for indoor mobile handling and manipulation platforms

A fundamental technology enabling the autonomous behavior of mobile robotics is navigation. It is a main prerequisite for mobile robotics to fulfill high-level tasks such as handling and manipulation, and is often identified as one of the key challenges in mobile robotics. The mapping and localization as the basis for navigation are intensively researched in the last few decades. However, there are still challenges or problems needed to be solved for online operating in large-scale environments or running on low-cost and energy-saving embedded systems. In this work, new developments and usages of Light Detection And Ranging (LiDAR) based Simultaneous Localization And Mapping (SLAM) algorithms are presented. A key component of LiDAR based SLAM algorithms, the scan matching algorithm, is explored. Different scan matching algorithms are systemically experimented with different LiDARs for indoor home-like environments for the first time. The influence of properties of LiDARs in scan matching algorithms is quantitatively analyzed. Improvements to Bayes filter based and graph optimization based SLAMs are presented. The Bayes filter based SLAMs mainly use the current sensor information to find the best estimation. A new efficient implementation of Rao-Blackwellized Particle Filter based SLAM is presented. It is based on a pre-computed lookup table and the parallelization of the particle updating. The new implementation runs efficiently on recent multi-core embedded systems that fulfill low cost and energy efficiency requirements. In contrast to Bayes filter based methods, graph optimization based SLAMs utilize all the sensor information and minimize the total error in the system. A new real-time graph building model and a robust integrated Graph SLAM solution are presented. The improvements include the definition of unique direction norms for points or lines extracted from scans, an efficient loop closure detection algorithm, and a parallel and adaptive implementation. The developed algorithm outperforms the state-of-the-art algorithms in processing time and robustness especially in large-scale environments using embedded systems instead of high-end computation devices. The results of the work can be used to improve the navigation system of indoor autonomous robots, like domestic environments and intra-logistics.Eine der grundlegenden Funktionen, welche die Autonomie in der mobilen Robotik ermöglicht, ist die Navigation. Sie ist eine wesentliche Voraussetzung dafür, dass mobile Roboter selbständig anspruchsvolle Aufgaben erfüllen können. Die Umsetzung der Navigation wird dabei oft als eine der wichtigsten Herausforderungen identifiziert. Die Kartenerstellung und Lokalisierung als Grundlage für die Navigation wurde in den letzten Jahrzehnten intensiv erforscht. Es existieren jedoch immer noch eine Reihe von Problemen, z.B. die Anwendung auf große Areale oder bei der Umsetzung auf kostengünstigen und energiesparenden Embedded-Systemen. Diese Arbeit stellt neue Ansätze und Lösungen im Bereich der LiDAR-basierten simultanen Positionsbestimmung und Kartenerstellung (SLAM) vor. Eine Schlüsselkomponente der LiDAR-basierten SLAM, die so genannten Scan-Matching-Algorithmen, wird näher untersucht. Verschiedene Scan-Matching-Algorithmen werden zum ersten Mal systematisch mit verschiedenen LiDARs für den Innenbereich getestet. Der Einfluss von LiDARs auf die Eigenschaften der Algorithmen wird quantitativ analysiert. Verbesserungen an Bayes-filterbasierten und graphoptimierten SLAMs werden in dieser Arbeit vorgestellt. Bayes-filterbasierte SLAMs verwenden hauptsächlich die aktuellen Sensorinformationen, um die beste Schätzung zu finden. Eine neue effiziente Implementierung des auf Partikel-Filter basierenden SLAM unter der Verwendung einer Lookup-Tabelle und der Parallelisierung wird vorgestellt. Die neue Implementierung kann effizient auf aktuellen Embedded-Systemen laufen. Im Gegensatz dazu verwenden Graph-SLAMs alle Sensorinformationen und minimieren den Gesamtfehler im System. Ein neues Echtzeitmodel für die Grafenerstellung und eine robuste integrierte SLAM-Lösung werden vorgestellt. Die Verbesserungen umfassen die Definition von eindeutigen Richtungsnormen für Scan, effiziente Algorithmen zur Erkennung von Loop Closures und eine parallele und adaptive Implementierung. Der entwickelte und auf eingebetteten Systemen eingesetzte Algorithmus übertrifft die aktuellen Algorithmen in Geschwindigkeit und Robustheit, insbesondere für große Areale. Die Ergebnisse der Arbeit können für die Verbesserung der Navigation von autonomen Robotern im Innenbereich, häuslichen Umfeld sowie der Intra-Logistik genutzt werden

PCA-based line detection from range data for mapping and localization-aiding of UAVs

This paper presents an original technique for robust detection of line features from range data, which is also the core element of an algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and attitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map. The innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation. Instead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on Least Squares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results demonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load and accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of the solutions proposed for line-based mapping and localization-aiding is evaluated exploiting real range data acquired in indoor environments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned vehicles moving in complex 2D areas, e.g. unexplored, full of obstacles, GPS-challenging or denied

Cost optimization in AGV applications

A otimização de custos em aplicações com veículos autónomos pode ser conseguida em diversas frentes. Nesta dissertação estudam-se e comparam-se soluções a três problemas: a interface entre instalador/operador do robô; a otimização de variáveis na solução de um problema de logística; e a escolha dos sensores afetos ao sistema de navegação

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

Evaluation of SLAM algorithms in realistic sensor test conditions

Autonomous robotic systems rely on Simultaneous Localisation and Mapping (SLAM) algorithms that use ranging or other sensory data as input to create a map of the environment. Numerous algorithms have been developed and demonstrated, many of which utilise data from high precision ranging instruments. Small Unmanned Aircraft Systems (UAS) have significant restrictions on the size and weight of sensors they can carry, and light-weight ranging sensors tend to be subject to greater error than their larger counterparts. The effect of these errors on the mapping capabilities of SLAM algorithms will depend on the combination of algorithm and sensor. To quantitatively determine the quality of the map, a map quality metric is needed. This thesis presents an evaluation of the mapping performance of a variety of SLAM algorithms that are freely available in the Robot Operating System (ROS), in conjunction with ranging data from various ranging sensors suitable for use onboard small UAS. To compare the quality of the generated maps, an existing metric was initially employed, however deficiencies noted in this metric led to the development of two new metrics. A discussion of both the existing and new map quality metrics, and the advantages and disadvantages of each, is presented as part of this thesis. To evaluate the performance of algorithm/sensor combinations, ranging data was collected from various sensors in a known environment. Both sensor poses and the ground truth map were obtained using a highly-accurate motion capture system. The measured sensor poses were then corrupted with noise and drift to simulate odometry measurements required for the SLAM algorithms. Of the SLAM algorithms tested, Gmapping was found to produce high quality maps with wide-field-of-regard range sensors in the presence of odometry noise and drift. KartoSLAM produced similar maps to Gmapping (with wide field of regard sensors), though it did not cope as well with odometry errors. Hector Mapping tends to excel at creating maps with wide field of regard ranging sensors