Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics

This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field

Visual Odometry and Traversability Analysis for Wheeled Robots in Complex Environments

Durch die technische Entwicklung im Bereich der radbasierten mobilen Roboter (WMRs) erweitern sich deren Anwendungsszenarien. Neben den eher strukturierten industriellen und häuslichen Umgebungen sind nun komplexere städtische Szenarien oder Außenbereiche mögliche Einsatzgebiete. Einer dieser neuen Anwendungsfälle wird in dieser Arbeit beschrieben: ein intelligenter persönlicher Mobilitätsassistent, basierend auf einem elektrischen Rollator. Ein solches System hat mehrere Anforderungen: Es muss sicher, robust, leicht und preiswert sein und sollte in der Lage sein, in Echtzeit zu navigieren, um eine direkte physische Interaktion mit dem Benutzer zu ermöglichen. Da diese Eigenschaften für fast alle Arten von WMRs wünschenswert sind, können alle in dieser Arbeit präsentierten Methoden auch mit anderen Typen von WMRs verwendet werden. Zuerst wird eine visuelle Odometriemethode vorgestellt, welche auf die Arbeit mit einer nach unten gerichteten RGB-D-Kamera ausgelegt ist. Hierzu wird die Umgebung auf die Bodenebene projiziert, um eine 2-dimensionale Repräsentation zu erhalten. Nun wird ein effizientes Bildausrichtungsverfahren verwendet, um die Fahrzeugbewegung aus aufeinander folgenden Bildern zu schätzen. Da das Verfahren für den Einsatz auf einem WMR ausgelegt ist, können weitere Annahmen verwendet werden, um die Genauigkeit der visuellen Odometrie zu verbessern. Für einen nicht-holonomischen WMR mit einem bekannten Fahrzeugmodell, entweder Differentialantrieb, Skid-Lenkung oder Ackermann-Lenkung, können die Bewegungsparameter direkt aus den Bilddaten geschätzt werden. Dies verbessert die Genauigkeit und Robustheit des Verfahrens erheblich. Zusätzlich wird eine Ausreißererkennung vorgestellt, die im Modellraum, d.h. den Bewegungsparametern des kinematischen Models, arbeitet. Üblicherweise wird die Ausreißererkennung im Datenraum, d.h. auf den Bildpunkten, durchgeführt. Mittels der Projektion der Umgebung auf die Bodenebene kann auch eine Höhenkarte der Umgebung erstellt werde. Es wird untersucht, ob diese Karte, in Verbindung mit einem detaillierten Fahrzeugmodell, zur Abschätzung zukünftiger Fahrzeugposen verwendet werden kann. Durch die Verwendung einer gemeinsamen bildbasierten Darstellung der Umgebung und des Fahrzeugs wird eine sehr effiziente und dennoch sehr genaue Posenschätzmethode vorgeschlagen. Da die Befahrbarkeit eines Bereichs durch die Fahrzeugposen und mögliche Kollisionen bestimmt werden kann, wird diese Methode für eine neue echtzeitfähige Pfadplanung verwendet. Aus der Fahrzeugpose werden verschiedene Sicherheitskriterien bestimmt, die als Heuristik für einen A*-ähnlichen Planer verwendet werden. Hierzu werden mithilfe des kinematischen Models mögliche zukünftige Fahrzeugposen ermittelt und für jede dieser Posen ein Befahrbarkeitswert berechnet.Das endgültige System ermöglicht eine sichere und robuste Echtzeit-Navigation auch in schwierigen Innen- und Außenumgebungen.The application of wheeled mobile robots (WMRs) is currently expanding from rather controlled industrial or domestic scenarios into more complex urban or outdoor environments, allowing a variety of new use cases. One of these new use cases is described in this thesis: An intelligent personal mobility assistant, based on an electrical rollator. Such a system comes with several requirements: It must be safe and robust, lightweight, inexpensive and should be able to navigate in real-time in order to allow direct physical interaction with the user. As these properties are desirable for most WMRs, all methods proposed in this thesis can also be used with other WMR platforms.First, a visual odometry method is presented, which is tailored to work with a downward facing RGB-D camera. It projects the environment onto a ground plane image and uses an efficient image alignment method to estimate the vehicle motion from consecutive images. As the method is designed for use on a WMR, further constraints can be employed to improve the accuracy of the visual odometry. For a non-holonomic WMR with a known vehicle model, either differential drive, skid steering or Ackermann, the motion parameters of the corresponding kinematic model, instead of the generic motion parameters, can be estimated directly from the image data. This significantly improves the accuracyand robustness of the method. Additionally, an outlier rejection scheme is presented that operates in model space, i.e. the motion parameters of the kinematic model, instead of data space, i.e. image pixels. Furthermore, the projection of the environment onto the ground plane can also be used to create an elevation map of the environment. It is investigated if this map, in conjunction with a detailed vehicle model, can be used to estimate future vehicle poses. By using a common image-based representation of the environment and the vehicle, a very efficient and still highly accurate pose estimation method is proposed. Since the traversability of an area can be determined by the vehicle poses and potential collisions, the pose estimation method is employed to create a novel real-time path planning method. The detailed vehicle model is extended to also represent the vehicle’s chassis for collision detection. Guided by an A*-like planner, a search graph is constructed by propagating the vehicle using its kinematic model to possible future poses and calculating a traversability score for each of these poses. The final system performs safe and robust real-time navigation even in challenging indoor and outdoor environments

LiDAR based multi-sensor fusion for localization, mapping, and tracking

Viimeisen vuosikymmenen aikana täysin itseohjautuvien ajoneuvojen kehitys on herättänyt laajaa kiinnostusta niin teollisuudessa kuin tiedemaailmassakin, mikä on merkittävästi edistänyt tilannetietoisuuden ja anturiteknologian kehitystä. Erityisesti LiDAR-anturit ovat nousseet keskeiseen rooliin monissa havainnointijärjestelmissä niiden tarjoaman pitkän kantaman havaintokyvyn, tarkan 3D-etäisyystiedon ja luotettavan suorituskyvyn ansiosta. LiDAR-teknologian kehittyminen on mahdollistanut entistä luotettavampien ja kustannustehokkaampien antureiden käytön, mikä puolestaan on osoittanut suurta potentiaalia parantaa laajasti käytettyjen kuluttajatuotteiden tilannetietoisuutta. Uusien LiDAR-antureiden hyödyntäminen tarjoaa tutkijoille monipuolisen valikoiman tehokkaita työkaluja, joiden avulla voidaan ratkaista paikannuksen, kartoituksen ja seurannan haasteita nykyisissä havaintojärjestelmissä. Tässä väitöskirjassa tutkitaan LiDAR-pohjaisia sensorifuusioalgoritmeja. Tutkimuksen pääpaino on tiheässä kartoituksessa ja globaalissa paikan-nuksessa erilaisten LiDAR-anturien avulla. Tutkimuksessa luodaan kattava tietokanta uusien LiDAR-, IMU- ja kamera-antureiden tuottamasta datasta. Tietokanta on välttämätön kehittyneiden anturifuusioalgoritmien ja yleiskäyttöisten paikannus- ja kartoitusalgoritmien kehittämiseksi. Tämän lisäksi väitöskirjassa esitellään innovatiivisia menetelmiä globaaliin paikannukseen erilaisissa ympäristöissä. Esitellyt menetelmät kartoituksen tarkkuuden ja tilannetietoisuuden parantamiseksi ovat muun muassa modulaarinen monen LiDAR-anturin odometria ja kartoitus, toimintavarma multimodaalinen LiDAR-inertiamittau-sjärjestelmä ja tiheä kartoituskehys. Tutkimus integroi myös kiinteät LiDAR -anturit kamerapohjaisiin syväoppimismenetelmiin kohteiden seurantaa varten parantaen kartoituksen tarkkuutta dynaamisissa ympäristöissä. Näiden edistysaskeleiden avulla autonomisten järjestelmien luotettavuutta ja tehokkuutta voidaan merkittävästi parantaa todellisissa käyttöympäristöissä. Väitöskirja alkaa esittelemällä innovatiiviset anturit ja tiedonkeruualustan. Tämän jälkeen esitellään avoin tietokanta, jonka avulla voidaan arvioida kehittyneitä paikannus- ja kartoitusalgoritmeja hyödyntäen ainutlaatuista perustotuuden kehittämismenetelmää. Työssä käsitellään myös kahta haastavaa paikannusympäristöä: metsä- ja kaupunkiympäristöä. Lisäksi tarkastellaan kohteen seurantatehtäviä sekä kameraettä LiDAR-tekniikoilla ihmisten ja pienten droonien seurannassa. ---------------------- The development of fully autonomous driving vehicles has become a key focus for both industry and academia over the past decade, fostering significant progress in situational awareness abilities and sensor technology. Among various types of sensors, the LiDAR sensor has emerged as a pivotal component in many perception systems due to its long-range detection capabilities, precise 3D range information, and reliable performance in diverse environments. With advancements in LiDAR technology, more reliable and cost-effective sensors have shown great potential for improving situational awareness abilities in widely used consumer products. By leveraging these novel LiDAR sensors, researchers now have a diverse set of powerful tools to effectively tackle the persistent challenges in localization, mapping, and tracking within existing perception systems. This thesis explores LiDAR-based sensor fusion algorithms to address perception challenges in autonomous systems, with a primary focus on dense mapping and global localization using diverse LiDAR sensors. The research involves the integration of novel LiDARs, IMU, and camera sensors to create a comprehensive dataset essential for developing advanced sensor fusion and general-purpose localization and mapping algorithms. Innovative methodologies for global localization across varied environments are introduced. These methodologies include a robust multi-modal LiDAR inertial odometry and a dense mapping framework, which enhance mapping precision and situational awareness. The study also integrates solid-state LiDARs with camera-based deep-learning techniques for object tracking, refining mapping accuracy in dynamic environments. These advancements significantly enhance the reliability and efficiency of autonomous systems in real-world scenarios. The thesis commences with an introduction to innovative sensors and a data collection platform. It proceeds by presenting an open-source dataset designed for the evaluation of advanced SLAM algorithms, utilizing a unique ground-truth generation method. Subsequently, the study tackles two localization challenges in forest and urban environments. Furthermore, it highlights the MM-LOAM dense mapping framework. Additionally, the research explores object-tracking tasks, employing both camera and LiDAR technologies for human and micro UAV tracking

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Abstract There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p

3D position tracking for all-terrain robots

Rough terrain robotics is a fast evolving field of research and a lot of effort is deployed towards enabling a greater level of autonomy for outdoor vehicles. Such robots find their application in scientific exploration of hostile environments like deserts, volcanoes, in the Antarctic or on other planets. They are also of high interest for search and rescue operations after natural or artificial disasters. The challenges to bring autonomy to all terrain rovers are wide. In particular, it requires the development of systems capable of reliably navigate with only partial information of the environment, with limited perception and locomotion capabilities. Amongst all the required functionalities, locomotion and position tracking are among the most critical. Indeed, the robot is not able to fulfill its task if an inappropriate locomotion concept and control is used, and global path planning fails if the rover loses track of its position. This thesis addresses both aspects, a) efficient locomotion and b) position tracking in rough terrain. The Autonomous System Lab developed an off-road rover (Shrimp) showing excellent climbing capabilities and surpassing most of the existing similar designs. Such an exceptional climbing performance enables an extension in the range of possible areas a robot could explore. In order to further improve the climbing capabilities and the locomotion efficiency, a control method minimizing wheel slip has been developed in this thesis. Unlike other control strategies, the proposed method does not require the use of soil models. Independence from these models is very significant because the ability to operate on different types of soils is the main requirement for exploration missions. Moreover, our approach can be adapted to any kind of wheeled rover and the processing power needed remains relatively low, which makes online computation feasible. In rough terrain, the problem of tracking the robot's position is tedious because of the excessive variation of the ground. Further, the field of view can vary significantly between two data acquisition cycles. In this thesis, a method for probabilistically combining different types of sensors to produce a robust motion estimation for an all-terrain rover is presented. The proposed sensor fusion scheme is flexible in that it can easily accommodate any number of sensors, of any kind. In order to test the algorithm, we have chosen to use the following sensory inputs for the experiments: 3D-Odometry, inertial measurement unit (accelerometers, gyros) and visual odometry. The 3D-Odometry has been specially developed in the framework of this research. Because it accounts for ground slope discontinuities and the rover kinematics, this technique results in a reasonably precise 3D motion estimate in rough terrain. The experiments provided excellent results and proved that the use of complementary sensors increases the robustness and accuracy of the pose estimate. In particular, this work distinguishes itself from other similar research projects in the following ways: the sensor fusion is performed with more than two sensor types and sensor fusion is applied a) in rough terrain and b) to track the real 3D pose of the rover. Another result of this work is the design of a high-performance platform for conducting further research. In particular, the rover is equipped with two computers, a stereovision module, an omnidirectional vision system, an inertial measurement unit, numerous sensors and actuators and electronics for power management. Further, a set of powerful tools has been developed to speed up the process of debugging algorithms and analyzing data stored during the experiments. Finally, the modularity and portability of the system enables easy adaptation of new actuators and sensors. All these characteristics speed up the research in this field

Toward lifelong visual localization and mapping

Thesis (Ph.D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-181).Mobile robotic systems operating over long durations require algorithms that are robust and scale efficiently over time as sensor information is continually collected. For mobile robots one of the fundamental problems is navigation; which requires the robot to have a map of its environment, so it can plan its path and execute it. Having the robot use its perception sensors to do simultaneous localization and mapping (SLAM) is beneficial for a fully autonomous system. Extending the time horizon of operations poses problems to current SLAM algorithms, both in terms of robustness and temporal scalability. To address this problem we propose a reduced pose graph model that significantly reduces the complexity of the full pose graph model. Additionally we develop a SLAM system using two different sensor modalities: imaging sonars for underwater navigation and vision based SLAM for terrestrial applications. Underwater navigation is one application domain that benefits from SLAM, where access to a global positioning system (GPS) is not possible. In this thesis we present SLAM systems for two underwater applications. First, we describe our implementation of real-time imaging-sonar aided navigation applied to in-situ autonomous ship hull inspection using the hovering autonomous underwater vehicle (HAUV). In addition we present an architecture that enables the fusion of information from both a sonar and a camera system. The system is evaluated using data collected during experiments on SS Curtiss and USCGC Seneca. Second, we develop a feature-based navigation system supporting multi-session mapping, and provide an algorithm for re-localizing the vehicle between missions. In addition we present a method for managing the complexity of the estimation problem as new information is received. The system is demonstrated using data collected with a REMUS vehicle equipped with a BlueView forward-looking sonar. The model we use for mapping builds on the pose graph representation which has been shown to be an efficient and accurate approach to SLAM. One of the problems with the pose graph formulation is that the state space continuously grows as more information is acquired. To address this problem we propose the reduced pose graph (RPG) model which partitions the space to be mapped and uses the partitions to reduce the number of poses used for estimation. To evaluate our approach, we present results using an online binocular and RGB-Depth visual SLAM system that uses place recognition both for robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping using approximately nine hours of data collected in the MIT Stata Center over the course of six months. Ground truth, derived by aligning laser scans to existing floor plans, is used to evaluate the global accuracy of the system. Our results illustrate the capability of our visual SLAM system to map a large scale environment over an extended period of time.by Hordur Johannsson.Ph.D