Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping

Modern 3D laser-range scanners have a high data rate, making online simultaneous localization and mapping (SLAM) computationally challenging. Recursive state estimation techniques are efficient but commit to a state estimate immediately after a new scan is made, which may lead to misalignments of measurements. We present a 3D SLAM approach that allows for refining alignments during online mapping. Our method is based on efficient local mapping and a hierarchical optimization back-end. Measurements of a 3D laser scanner are aggregated in local multiresolution maps by means of surfel-based registration. The local maps are used in a multi-level graph for allocentric mapping and localization. In order to incorporate corrections when refining the alignment, the individual 3D scans in the local map are modeled as a sub-graph and graph optimization is performed to account for drift and misalignments in the local maps. Furthermore, in each sub-graph, a continuous-time representation of the sensor trajectory allows to correct measurements between scan poses. We evaluate our approach in multiple experiments by showing qualitative results. Furthermore, we quantify the map quality by an entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201

Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments

The use of multiple aerial vehicles for autonomous missions is turning into commonplace. In many of these applications, the Unmanned Aerial Vehicles (UAVs) have to cooperate and navigate in a shared airspace, becoming 3D collision avoidance a relevant issue. Outdoor scenarios impose additional challenges: (i) accurate positioning systems are costly; (ii) communication can be unreliable or delayed; and (iii) external conditions like wind gusts affect UAVs’ maneuverability. In this paper, we present 3D-SWAP, a decentralized algorithm for 3D collision avoidance with multiple UAVs. 3D-SWAP operates reactively without high computational requirements and allows UAVs to integrate measurements from their local sensors with positions of other teammates within communication range. We tested 3D-SWAP with our team of custom-designed UAVs. First, we used a Software-In-The-Loop simulator for system integration and evaluation. Second, we run field experiments with up to three UAVs in an outdoor scenario with uncontrolled conditions (i.e., noisy positioning systems, wind gusts, etc). We report our results and our procedures for this field experimentation.European Union’s Horizon 2020 research and innovation programme No 731667 (MULTIDRONE

Behaviour-driven motion synthesis

Heightened demand for alternatives to human exposure to strenuous and repetitive labour, as well as to hazardous environments, has led to an increased interest in real-world deployment of robotic agents. Targeted applications require robots to be adept at synthesising complex motions rapidly across a wide range of tasks and environments. To this end, this thesis proposes leveraging abstractions of the problem at hand to ease and speed up the solving. We formalise abstractions to hint relevant robotic behaviour to a family of planning problems, and integrate them tightly into the motion synthesis process to make real-world deployment in complex environments practical. We investigate three principal challenges of this proposition. Firstly, we argue that behavioural samples in form of trajectories are of particular interest to guide robotic motion synthesis. We formalise a framework with behavioural semantic annotation that enables the storage and bootstrap of sets of problem-relevant trajectories. Secondly, in the core of this thesis, we study strategies to exploit behavioural samples in task instantiations that differ significantly from those stored in the framework. We present two novel strategies to efficiently leverage offline-computed problem behavioural samples: (i) online modulation based on geometry-tuned potential fields, and (ii) experience-guided exploration based on trajectory segmentation and malleability. Thirdly, we demonstrate that behavioural hints can be extracted on-the-fly to tackle highlyconstrained, ever-changing complex problems, from which there is no prior knowledge. We propose a multi-layer planner that first solves a simplified version of the problem at hand, to then inform the search for a solution in the constrained space. Our contributions on efficient motion synthesis via behaviour guidance augment the robots’ capabilities to deal with more complex planning problems, and do so more effectively than related approaches in the literature by computing better quality paths in lower response time. We demonstrate our contributions, in both laboratory experiments and field trials, on a spectrum of planning problems and robotic platforms ranging from high-dimensional humanoids and robotic arms with a focus on autonomous manipulation in resembling environments, to high-dimensional kinematic motion planning with a focus on autonomous safe navigation in unknown environments. While this thesis was motivated by challenges on motion synthesis, we have explored the applicability of our findings on disparate robotic fields, such as grasp and task planning. We have made some of our contributions open-source hoping they will be of use to the robotics community at large.The CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of EdinburghThe ORCA Hub EPSRC project (EP/R026173/1)The Scottish Informatics and Computer Science Alliance (SICSA

Aerial Semantic Mapping for Precision Agriculture using Multispectral Imagery

Nowadays constant technological evolution cover several necessities and daily tasks in our society. In particular, drones usage, given its wide vision to capture the terrain surface images, allows to collect large amounts of information with high efficiency, performance and accuracy. This master dissertation’s main purpose is the analysis, classification and respective mapping of different terrain types and characteristics, using multispectral imagery. Solar radiation flow reflected on the surface is captured by the used multispectral camera’s different lenses (RedEdge-M, created by Micasense). Each one of these five lenses is able to capture different colour spectrums (i.e. Blue, Green, Red, Near-Infrared and RedEdge). It is possible to analyse the various spectrum indices from the collected imagery, according to the fusion of different combinations between coloured bands (e.g. NDVI, ENDVI, RDVI. . . ). This project engages a ROS (Robot Operating System) framework development, capable of correcting different captured imagery and, hence, calculating the implemented spectral indices. Several parametrizations of terrain analysis were carried throughout the project, and this information was represented in semantic maps by layers (e.g. vegetation, water, soil, rocks). The obtained experimental results were validated in the scope of several projects incorporated in PDR2020, with success rates between 70% and 90%. This framework can have multiple technical applications, not only in Precision Agriculture, but also in vehicles autonomous navigation and multi-robot cooperation

Planning and Navigation in Dynamic Environments for Mobile Robots and Micro Aerial Vehicles

Reliable and robust navigation planning and obstacle avoidance is key for the autonomous operation of mobile robots. In contrast to stationary industrial robots that often operate in controlled spaces, planning for mobile robots has to take changing environments and uncertainties into account during plan execution. In this thesis, planning and obstacle avoidance techniques are proposed for a variety of ground and aerial robots. Common to most of the presented approaches is the exploitation of the nature of the underlying problem to achieve short planning times by using multiresolution or hierarchical approaches. Short planning times allow for continuous and fast replanning to take the uncertainty in the environment and robot motion execution into account. The proposed approaches are evaluated in simulation and real-world experiments. The first part of this thesis addresses planning for mobile ground robots. One contribution is an approach to grasp and object removal planning to pick objects from a transport box with a mobile manipulation robot. In a multistage process, infeasible grasps are pruned in offline and online processing steps. Collision-free endeffector trajectories are planned to the remaining grasps until a valid removal trajectory can be found. An object-centric local multiresolution representation accelerates trajectory planning. The mobile manipulation components are evaluated in an integrated mobile bin-picking system. Local multiresolution planning is employed for path planning for humanoid soccer robots as well. The used Nao robot is equipped with only relatively low computing power. A resource-efficient path planner including the anticipated movements of opponents on the field is developed as part of this thesis. In soccer games an important subproblem is to reach a position behind the ball to dribble or kick it towards the goal. By the assumption that the opponents have the same intention, an explicit representation of their movements is possible. This leads to paths that facilitate the robot to reach its target position with a higher probability without being disturbed by the other robot. The evaluation for the planner is performed in a physics-based soccer simulation. The second part of this thesis covers planning and obstacle avoidance for micro aerial vehicles (MAVs), in particular multirotors. To reduce the planning complexity, the planning problem is split into a hierarchy of planners running on different levels of abstraction, i.e., from abstract to detailed environment descriptions and from coarse to fine plans. A complete planning hierarchy for MAVs is presented, from mission planners for multiple application domains to low-level obstacle avoidance. Missions planned on the top layer are executed by means of coupled allocentric and egocentric path planning. Planning is accelerated by global and local multiresolution representations. The planners can take multiple objectives into account in addition to obstacle costs and path length, e.g., sensor constraints. The path planners are supplemented by trajectory optimization to achieve dynamically feasible trajectories that can be executed by the underlying controller at higher velocities. With the initialization techniques presented in this thesis, the convergence of the optimization problem is expedited. Furthermore, frequent reoptimization of the initial trajectory allows for the reaction to changes in the environment without planning and optimizing a complete new trajectory. Fast, reactive obstacle avoidance based on artificial potential fields acts as a safety layer in the presented hierarchy. The obstacle avoidance layer employs egocentric sensor data and can operate at the data acquisition frequency of up to 40 Hz. It can slow-down and stop the MAVs in front of obstacles as well as avoid approaching dynamic obstacles. We evaluate our planning and navigation hierarchy in simulation and with a variety of MAVs in real-world applications, especially outdoor mapping missions, chimney and building inspection, and automated stocktaking.Planung und Navigation in dynamischen Umgebungen für mobile Roboter und Multikopter Zuverlässige und sichere Navigationsplanung und Hindernisvermeidung ist ein wichtiger Baustein für den autonomen Einsatz mobiler Roboter. Im Gegensatz zu klassischen Industrierobotern, die in der Regel in abgetrennten, kontrollierten Bereichen betrieben werden, ist es in der mobilen Robotik unerlässlich, Änderungen in der Umgebung und die Unsicherheit bei der Aktionsausführung zu berücksichtigen. Im Rahmen dieser Dissertation werden Verfahren zur Planung und Hindernisvermeidung für eine Reihe unterschiedlicher Boden- und Flugroboter entwickelt und vorgestellt. Den meisten beschriebenen Ansätzen ist gemein, dass die Struktur der zu lösenden Probleme ausgenutzt wird, um Planungsprozesse zu beschleunigen. Häufig ist es möglich, mit abnehmender Genauigkeit zu planen desto weiter eine Aktion in der Zeit oder im Ort entfernt ist. Dieser Ansatz wird lokale Multiresolution genannt. In anderen Fällen ist eine Zerlegung des Problems in Schichten unterschiedlicher Genauigkeit möglich. Die damit zu erreichende Beschleunigung der Planung ermöglicht ein häufiges Neuplanen und somit die Reaktion auf Änderungen in der Umgebung und Abweichungen bei den ausgeführten Aktionen. Zur Evaluation der vorgestellten Ansätze werden Experimente sowohl in der Simulation als auch mit Robotern durchgeführt. Der erste Teil dieser Dissertation behandelt Planungsmethoden für mobile Bodenroboter. Um Objekte mit einem mobilen Roboter aus einer Transportkiste zu greifen und zur Weiterverarbeitung zu einem Arbeitsplatz zu liefern, wurde ein System zur Planung möglicher Greifposen und hindernisfreier Endeffektorbahnen entwickelt. In einem mehrstufigen Prozess werden mögliche Griffe an bekannten Objekten erst in mehreren Vorverarbeitungsschritten (offline) und anschließend, passend zu den erfassten Objekten, online identifiziert. Zu den verbleibenden möglichen Griffen werden Endeffektorbahnen geplant und, bei Erfolg, ausgeführt. Die Greif- und Bahnplanung wird durch eine objektzentrische lokale Multiresolutionskarte beschleunigt. Die Einzelkomponenten werden in einem prototypischen Gesamtsystem evaluiert. Eine weitere Anwendung für die lokale Multiresolutionsplanung ist die Pfadplanung für humanoide Fußballroboter. Zum Einsatz kommen Nao-Roboter, die nur über eine sehr eingeschränkte Rechenleistung verfügen. Durch die Reduktion der Planungskomplexität mit Hilfe der lokalen Multiresolution, wurde die Entwicklung eines Planers ermöglicht, der zusätzlich zur aktuellen Hindernisfreiheit die Bewegung der Gegenspieler auf dem Feld berücksichtigt. Hierbei liegt der Fokus auf einem wichtigen Teilproblem, dem Erreichen einer guten Schussposition hinter dem Ball. Die Tatsache, dass die Gegenspieler vergleichbare Ziele verfolgen, ermöglicht es, Annahmen über mögliche Laufwege zu treffen. Dadurch ist die Planung von Pfaden möglich, die das Risiko, durch einen Gegenspieler passiv geblockt zu werden, reduzieren, so dass die Schussposition schneller erreicht wird. Dieser Teil der Arbeit wird in einer physikalischen Fußballsimulation evaluiert. Im zweiten Teil dieser Dissertation werden Methoden zur Planung und Hindernisvermeidung von Multikoptern behandelt. Um die Planungskomplexität zu reduzieren, wird das zu lösenden Planungsproblem hierarchisch zerlegt und durch verschiedene Planungsebenen verarbeitet. Dabei haben höhere Planungsebenen eine abstraktere Weltsicht und werden mit niedriger Frequenz ausgeführt, zum Beispiel die Missionsplanung. Niedrigere Ebenen haben eine Weltsicht, die mehr den Sensordaten entspricht und werden mit höherer Frequenz ausgeführt. Die Granularität der resultierenden Pläne verfeinert sich hierbei auf niedrigeren Ebenen. Im Rahmen dieser Dissertation wurde eine komplette Planungshierarchie für Multikopter entwickelt, von Missionsplanern für verschiedene Anwendungsgebiete bis zu schneller Hindernisvermeidung. Pfade zur Ausführung geplanter Missionen werden durch zwei gekoppelte Planungsebenen erstellt, erst allozentrisch, und dann egozentrisch verfeinert. Hierbei werden ebenfalls globale und lokale Multiresolutionsrepräsentationen zur Beschleunigung der Planung eingesetzt. Zusätzlich zur Hindernisfreiheit und Länge der Pfade können auf diesen Planungsebenen weitere Zielfunktionen berücksichtigt werden, wie zum Beispiel die Berücksichtigung von Sensorcharakteristika. Ergänzt werden die Planungsebenen durch die Optimierung von Flugbahnen. Diese Flugbahnen berücksichtigen eine angenäherte Flugdynamik und erlauben damit ein schnelleres Verfolgen der optimierten Pfade. Um eine schnelle Konvergenz des Optimierungsproblems zu erreichen, wurde in dieser Arbeit ein Verfahren zur Initialisierung entwickelt. Des Weiteren kommen Methoden zur schnellen Verfeinerung des Optimierungsergebnisses bei Änderungen im Weltzustand zum Einsatz, diese ermöglichen die Reaktion auf neue Hindernisse oder Abweichungen von der Flugbahn, ohne eine komplette Flugbahn neu zu planen und zu optimieren. Die Sicherheit des durch die Planungs- und Optimierungsebenen erstellten Pfades wird durch eine schnelle, reaktive Hindernisvermeidung gewährleistet. Das Hindernisvermeidungsmodul basiert auf der Methode der künstlichen Potentialfelder. Durch die Verwendung dieser schnellen Methode kombiniert mit der Verwendung von nicht oder nur über kurze Zeiträume aggregierte Sensordaten, ermöglicht die Reaktion auf unbekannte Hindernisse, kurz nachdem diese von den Sensoren wahrgenommen wurden. Dabei kann der Multikopter abgebremst oder gestoppt werden, und sich von nähernden Hindernissen entfernen. Die Komponenten der Planungs- und Hindernisvermeidungshierarchie werden sowohl in der Simulation evaluiert, als auch in integrierten Gesamtsystemen mit verschiedenen Multikoptern in realen Anwendungen. Dies sind insbesondere die Kartierung von Innen- und Außenbereichen, die Inspektion von Gebäuden und Schornsteinen sowie die automatisierte Inventur von Lägern

GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction

This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Those properties facilitate robust data association and map updating in our scan-to-map registration scheme, especially when working with sparse range data. Compared with previous GP-SLAM, this work overcomes the prohibitive computational complexity of GP and redesigns the registration strategy to meet the accuracy requirements in 3D scenarios. For large-scale tasks, a two-thread framework is employed to suppress the drift further. Aerial and ground-based experiments demonstrate that our method allows robust odometry and precise mapping in real-time. It also outperforms the state-of-the-art lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%