Cartographer slam method for optimization with an adaptive multi-distance scan scheduler

This paper presents the use of Google's simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help ofscheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scanswhile maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose's generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy

Robotic Cameraman for Augmented Reality based Broadcast and Demonstration

In recent years, a number of large enterprises have gradually begun to use vari-ous Augmented Reality technologies to prominently improve the audiences’ view oftheir products. Among them, the creation of an immersive virtual interactive scenethrough the projection has received extensive attention, and this technique refers toprojection SAR, which is short for projection spatial augmented reality. However,as the existing projection-SAR systems have immobility and limited working range,they have a huge difficulty to be accepted and used in human daily life. Therefore,this thesis research has proposed a technically feasible optimization scheme so thatit can be practically applied to AR broadcasting and demonstrations. Based on three main techniques required by state-of-art projection SAR applica-tions, this thesis has created a novel mobile projection SAR cameraman for ARbroadcasting and demonstration. Firstly, by combining the CNN scene parsingmodel and multiple contour extractors, the proposed contour extraction pipelinecan always detect the optimal contour information in non-HD or blurred images.This algorithm reduces the dependency on high quality visual sensors and solves theproblems of low contour extraction accuracy in motion blurred images. Secondly, aplane-based visual mapping algorithm is introduced to solve the difficulties of visualmapping in these low-texture scenarios. Finally, a complete process of designing theprojection SAR cameraman robot is introduced. This part has solved three mainproblems in mobile projection-SAR applications: (i) a new method for marking con-tour on projection model is proposed to replace the model rendering process. Bycombining contour features and geometric features, users can identify objects oncolourless model easily. (ii) a camera initial pose estimation method is developedbased on visual tracking algorithms, which can register the start pose of robot to thewhole scene in Unity3D. (iii) a novel data transmission approach is introduced to establishes a link between external robot and the robot in Unity3D simulation work-space. This makes the robotic cameraman can simulate its trajectory in Unity3D simulation work-space and project correct virtual content. Our proposed mobile projection SAR system has made outstanding contributionsto the academic value and practicality of the existing projection SAR technique. Itfirstly solves the problem of limited working range. When the system is running ina large indoor scene, it can follow the user and project dynamic interactive virtualcontent automatically instead of increasing the number of visual sensors. Then,it creates a more immersive experience for audience since it supports the user hasmore body gestures and richer virtual-real interactive plays. Lastly, a mobile systemdoes not require up-front frameworks and cheaper and has provided the public aninnovative choice for indoor broadcasting and exhibitions

RGB-D And Thermal Sensor Fusion: A Systematic Literature Review

In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensive evaluation of fusing RGB-D and thermal modalities to date. While autonomous driving using LiDAR, radar, RGB, and other sensors has garnered substantial research interest, along with the fusion of RGB and depth modalities, the integration of thermal cameras and, specifically, the fusion of RGB-D and thermal data, has received comparatively less attention. This might be partly due to the limited number of publicly available datasets for such applications. This paper provides a comprehensive review of both, state-of-the-art and traditional methods used in fusing RGB-D and thermal camera data for various applications, such as site inspection, human tracking, fault detection, and others. The reviewed literature has been categorised into technical areas, such as 3D reconstruction, segmentation, object detection, available datasets, and other related topics. Following a brief introduction and an overview of the methodology, the study delves into calibration and registration techniques, then examines thermal visualisation and 3D reconstruction, before discussing the application of classic feature-based techniques as well as modern deep learning approaches. The paper concludes with a discourse on current limitations and potential future research directions. It is hoped that this survey will serve as a valuable reference for researchers looking to familiarise themselves with the latest advancements and contribute to the RGB-DT research field.Comment: 33 pages, 20 figure

Localisation of ground range sensors using overhead imagery

This thesis is about outdoor localisation using range sensors as an active sensor and cheap, publicly available satellite or `overhead' imagery as a prior map. Range sensors such as lidars and spinning FMCW radars are ideal for large-scale, outdoor autonomous navigation due to their long sensing range, invariance to lighting conditions, and robustness against weather changes. Nevertheless, existing methods for range sensor localisation typically rely on prior maps collected from a previous mapping phase. On the other hand, off-the-shelf overhead imagery, such as public satellite images, is readily available almost anywhere in the world and can be acquired easily from the internet with little cost or effort. Public overhead imagery can capture geometric cues of the scene also observable by ground lidars and radars, therefore having the capability to act as a map for range sensor localisation. In particular, in corner case scenarios where the prior sensory map is unusable or unavailable, for example if the robot travels to a place it has not visited before, public overhead imagery can act as an alternative map source for range sensor localisation as a fall-back choice. Under normal operation conditions, the localisation result by comparing range sensor data against overhead imagery can act as an additional information source for redundancy. In this thesis, we present various methods to solve the localisation of a ground range sensor using overhead imagery by learning from data, enabling them to adapt to different environments. This surpasses the methods in literature which employ hand-crafted features designed for only specific types of scenery. Specifically, we address both topological localisation, also known as place recognition, and metric localisation in overhead imagery maps. Furthermore, we investigate self-supervised strategies that allow the tasks to be learned without accurate ground truth data

Semantic Localization and Mapping in Robot Vision

Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks. This dissertation presents three solutions which incorporate semantics onto visual data in order to address these problems. First, on the problem of constructing topological maps from sequence of images. The proposed solution includes a novel image similarity score which uses dynamic programming to match images using both appearance and relative positions of local features simultaneously. An MRF is constructed to model the probability of loop-closures and a locally optimal labeling is found using Loopy-BP. The recovered loop closures are then used to generate a topological map. Results are presented on four urban sequences and one indoor sequence. The second system uses video and annotated maps to solve localization. Data association is achieved through detection of object classes, annotated in prior maps, rather than through detection of visual features. To avoid the caveats of object recognition, a new representation of query images is introduced consisting of a vector of detection scores for each object class. Using soft object detections, hypotheses about pose are refined through particle filtering. Experiments include both small office spaces, and a large open urban rail station with semantically ambiguous places. This approach showcases a representation that is both robust and can exploit the plethora of existing prior maps for GPS-denied environments while avoiding the data association problems encountered when matching point clouds or visual features. Finally, a purely vision-based approach for constructing semantic maps given camera pose and simple object exemplar images. Object response heatmaps are combined with known pose to back-project detection information onto the world. These update the world model, integrating information over time as the camera moves. The approach avoids making hard decisions on object recognition, and aggregates evidence about objects in the world coordinate system. These solutions simultaneously showcase the contribution of semantics in robotics and provide state of the art solutions to these fundamental problems

Advanced LIDAR-based techniques for autonomous navigation of spaceborne and airborne platforms

The main goal of this PhD thesis is the development and performance assessment of innovative techniques for the autonomous navigation of aerospace platforms by exploiting data acquired by electro-optical sensors. Specifically, the attention is focused on active LIDAR systems since they globally provide a higher degree of autonomy with respect to passive sensors. Two different areas of research are addressed, namely the autonomous relative navigation of multi-satellite systems and the autonomous navigation of Unmanned Aerial Vehicles. The global aim is to provide solutions able to improve estimation accuracy, computational load, and overall robustness and reliability with respect to the techniques available in the literature. In the space field, missions like on-orbit servicing and active debris removal require a chaser satellite to perform autonomous orbital maneuvers in close-proximity of an uncooperative space target. In this context, a complete pose determination architecture is here proposed, which relies exclusively on three-dimensional measurements (point clouds) provided by a LIDAR system as well as on the knowledge of the target geometry. Customized solutions are envisaged at each step of the pose determination process (acquisition, tracking, refinement) to ensure adequate accuracy level while simultaneously limiting the computational load with respect to other approaches available in the literature. Specific strategies are also foreseen to ensure process robustness by autonomously detecting algorithms' failures. Performance analysis is realized by means of a simulation environment which is conceived to realistically reproduce LIDAR operation, target geometry, and multi-satellite relative dynamics in close-proximity. An innovative method to design trajectories for target monitoring, which are reliable for on-orbit servicing and active debris removal applications since they satisfy both safety and observation requirements, is also presented. On the other hand, the problem of localization and mapping of Unmanned Aerial Vehicles is also tackled since it is of utmost importance to provide autonomous safe navigation capabilities in mission scenarios which foresee flights in complex environments, such as GPS denied or challenging. Specifically, original solutions are proposed for the localization and mapping steps based on the integration of LIDAR and inertial data. Also in this case, particular attention is focused on computational load and robustness issues. Algorithms' performance is evaluated through off-line simulations carried out on the basis of experimental data gathered by means of a purposely conceived setup within an indoor test scenario

Multiple View Texture Mapping: A Rendering Approach Designed for Driving Simulation

Simulation provides a safe and controlled environment ideal for human testing [49, 142, 120]. Simulation of real environments has reached new heights in terms of photo-realism. Often, a team of professional graphical artists would have to be hired to compete with modern commercial simulators. Meanwhile, machine vision methods are currently being developed that attempt to automatically provide geometrically consistent and photo-realistic 3D models of real scenes [189, 139, 115, 19, 140, 111, 132]. Often the only requirement is a set of images of that scene. A road engineer wishing to simulate the environment of a real road for driving experiments could potentially use these tools. This thesis develops a driving simulator that uses machine vision methods to reconstruct a real road automatically. A computer graphics method called projective texture mapping is applied to enhance the photo-realism of the 3D models[144, 43]. This essentially creates a virtual projector in the 3D environment to automatically assign image coordinates to a 3D model. These principles are demonstrated using custom shaders developed for an OpenGL rendering pipeline. Projective texture mapping presents a list of challenges to overcome, these include reverse projection and projection onto surfaces not immediately in front of the projector [53]. A significant challenge was the removal of dynamic foreground objects. 3D reconstruction systems create 3D models based on static objects captured in images. Dynamic objects are rarely reconstructed. Projective texture mapping of images, including these dynamic objects, can result in visual artefacts. A workflow is developed to resolve this, resulting in videos and 3D reconstructions of streets with no moving vehicles on the scene. The final simulator using 3D reconstruction and projective texture mapping is then developed. The rendering camera had a motion model introduced to enable human interaction. The final system is presented, experimentally tested, and future potential works are discussed

Towards markerless orthopaedic navigation with intuitive Optical See-through Head-mounted displays

The potential of image-guided orthopaedic navigation to improve surgical outcomes has been well-recognised during the last two decades. According to the tracked pose of target bone, the anatomical information and preoperative plans are updated and displayed to surgeons, so that they can follow the guidance to reach the goal with higher accuracy, efficiency and reproducibility. Despite their success, current orthopaedic navigation systems have two main limitations: for target tracking, artificial markers have to be drilled into the bone and calibrated manually to the bone, which introduces the risk of additional harm to patients and increases operating complexity; for guidance visualisation, surgeons have to shift their attention from the patient to an external 2D monitor, which is disruptive and can be mentally stressful. Motivated by these limitations, this thesis explores the development of an intuitive, compact and reliable navigation system for orthopaedic surgery. To this end, conventional marker-based tracking is replaced by a novel markerless tracking algorithm, and the 2D display is replaced by a 3D holographic Optical see-through (OST) Head-mounted display (HMD) precisely calibrated to a user's perspective. Our markerless tracking, facilitated by a commercial RGBD camera, is achieved through deep learning-based bone segmentation followed by real-time pose registration. For robust segmentation, a new network is designed and efficiently augmented by a synthetic dataset. Our segmentation network outperforms the state-of-the-art regarding occlusion-robustness, device-agnostic behaviour, and target generalisability. For reliable pose registration, a novel Bounded Iterative Closest Point (BICP) workflow is proposed. The improved markerless tracking can achieve a clinically acceptable error of 0.95 deg and 2.17 mm according to a phantom test. OST displays allow ubiquitous enrichment of perceived real world with contextually blended virtual aids through semi-transparent glasses. They have been recognised as a suitable visual tool for surgical assistance, since they do not hinder the surgeon's natural eyesight and require no attention shift or perspective conversion. The OST calibration is crucial to ensure locational-coherent surgical guidance. Current calibration methods are either human error-prone or hardly applicable to commercial devices. To this end, we propose an offline camera-based calibration method that is highly accurate yet easy to implement in commercial products, and an online alignment-based refinement that is user-centric and robust against user error. The proposed methods are proven to be superior to other similar State-of- the-art (SOTA)s regarding calibration convenience and display accuracy. Motivated by the ambition to develop the world's first markerless OST navigation system, we integrated the developed markerless tracking and calibration scheme into a complete navigation workflow designed for femur drilling tasks during knee replacement surgery. We verify the usability of our designed OST system with an experienced orthopaedic surgeon by a cadaver study. Our test validates the potential of the proposed markerless navigation system for surgical assistance, although further improvement is required for clinical acceptance.Open Acces

Binokulare Eigenbewegungsschätzung für Fahrerassistenzanwendungen

Driving can be dangerous. Humans become inattentive when performing a monotonous task like driving. Also the risk implied while multi-tasking, like using the cellular phone while driving, can break the concentration of the driver and increase the risk of accidents. Others factors like exhaustion, nervousness and excitement affect the performance of the driver and the response time. Consequently, car manufacturers have developed systems in the last decades which assist the driver under various circumstances. These systems are called driver assistance systems. Driver assistance systems are meant to support the task of driving, and the field of action varies from alerting the driver, with acoustical or optical warnings, to taking control of the car, such as keeping the vehicle in the traffic lane until the driver resumes control. For such a purpose, the vehicle is equipped with on-board sensors which allow the perception of the environment and/or the state of the vehicle. Cameras are sensors which extract useful information about the visual appearance of the environment. Additionally, a binocular system allows the extraction of 3D information. One of the main requirements for most camera-based driver assistance systems is the accurate knowledge of the motion of the vehicle. Some sources of information, like velocimeters and GPS, are of common use in vehicles today. Nevertheless, the resolution and accuracy usually achieved with these systems are not enough for many real-time applications. The computation of ego-motion from sequences of stereo images for the implementation of driving intelligent systems, like autonomous navigation or collision avoidance, constitutes the core of this thesis. This dissertation proposes a framework for the simultaneous computation of the 6 degrees of freedom of ego-motion (rotation and translation in 3D Euclidean space), the estimation of the scene structure and the detection and estimation of independently moving objects. The input is exclusively provided by a binocular system and the framework does not call for any data acquisition strategy, i.e. the stereo images are just processed as they are provided. Stereo allows one to establish correspondences between left and right images, estimating 3D points of the environment via triangulation. Likewise, feature tracking establishes correspondences between the images acquired at different time instances. When both are used together for a large number of points, the result is a set of clouds of 3D points with point-to-point correspondences between clouds. The apparent motion of the 3D points between consecutive frames is caused by a variety of reasons. The most dominant motion for most of the points in the clouds is caused by the ego-motion of the vehicle; as the vehicle moves and images are acquired, the relative position of the world points with respect to the vehicle changes. Motion is also caused by objects moving in the environment. They move independently of the vehicle motion, so the observed motion for these points is the sum of the ego-vehicle motion and the independent motion of the object. A third reason, and of paramount importance in vision applications, is caused by correspondence problems, i.e. the incorrect spatial or temporal assignment of the point-to-point correspondence. Furthermore, all the points in the clouds are actually noisy measurements of the real unknown 3D points of the environment. Solving ego-motion and scene structure from the clouds of points requires some previous analysis of the noise involved in the imaging process, and how it propagates as the data is processed. Therefore, this dissertation analyzes the noise properties of the 3D points obtained through stereo triangulation. This leads to the detection of a bias in the estimation of 3D position, which is corrected with a reformulation of the projection equation. Ego-motion is obtained by finding the rotation and translation between the two clouds of points. This problem is known as absolute orientation, and many solutions based on least squares have been proposed in the literature. This thesis reviews the available closed form solutions to the problem. The proposed framework is divided in three main blocks: 1) stereo and feature tracking computation, 2) ego-motion estimation and 3) estimation of 3D point position and 3D velocity. The first block solves the correspondence problem providing the clouds of points as output. No special implementation of this block is required in this thesis. The ego-motion block computes the motion of the cameras by finding the absolute orientation between the clouds of static points in the environment. Since the cloud of points might contain independently moving objects and outliers generated by false correspondences, the direct computation of the least squares might lead to an erroneous solution. The first contribution of this thesis is an effective rejection rule that detects outliers based on the distance between predicted and measured quantities, and reduces the effects of noisy measurement by assigning appropriate weights to the data. This method is called Smoothness Motion Constraint (SMC). The ego-motion of the camera between two frames is obtained finding the absolute orientation between consecutive clouds of weighted 3D points. The complete ego-motion since initialization is achieved concatenating the individual motion estimates. This leads to a super-linear propagation of the error, since noise is integrated. A second contribution of this dissertation is a predictor/corrector iterative method, which integrates the clouds of 3D points of multiple time instances for the computation of ego-motion. The presented method considerably reduces the accumulation of errors in the estimated ego-position of the camera. Another contribution of this dissertation is a method which recursively estimates the 3D world position of a point and its velocity; by fusing stereo, feature tracking and the estimated ego-motion in a Kalman Filter system. An improved estimation of point position is obtained this way, which is used in the subsequent system cycle resulting in an improved computation of ego-motion. The general contribution of this dissertation is a single framework for the real time computation of scene structure, independently moving objects and ego-motion for automotive applications.Autofahren kann gefährlich sein. Die Fahrleistung wird durch die physischen und psychischen Grenzen des Fahrers und durch externe Faktoren wie das Wetter beeinflusst. Fahrerassistenzsysteme erhöhen den Fahrkomfort und unterstützen den Fahrer, um die Anzahl an Unfällen zu verringern. Fahrerassistenzsysteme unterstützen den Fahrer durch Warnungen mit optischen oder akustischen Signalen bis hin zur Übernahme der Kontrolle über das Auto durch das System. Eine der Hauptvoraussetzungen für die meisten Fahrerassistenzsysteme ist die akkurate Kenntnis der Bewegung des eigenen Fahrzeugs. Heutzutage verfügt man über verschiedene Sensoren, um die Bewegung des Fahrzeugs zu messen, wie zum Beispiel GPS und Tachometer. Doch Auflösung und Genauigkeit dieser Systeme sind nicht ausreichend für viele Echtzeitanwendungen. Die Berechnung der Eigenbewegung aus Stereobildsequenzen für Fahrerassistenzsysteme, z.B. zur autonomen Navigation oder Kollisionsvermeidung, bildet den Kern dieser Arbeit. Diese Dissertation präsentiert ein System zur Echtzeitbewertung einer Szene, inklusive Detektion und Bewertung von unabhängig bewegten Objekten sowie der akkuraten Schätzung der sechs Freiheitsgrade der Eigenbewegung. Diese grundlegenden Bestandteile sind erforderlich, um viele intelligente Automobilanwendungen zu entwickeln, die den Fahrer in unterschiedlichen Verkehrssituationen unterstützen. Das System arbeitet ausschließlich mit einer Stereokameraplattform als Sensor. Um die Eigenbewegung und die Szenenstruktur zu berechnen wird eine Analyse des Rauschens und der Fehlerfortpflanzung im Bildaufbereitungsprozess benötigt. Deshalb werden in dieser Dissertation die Rauscheigenschaften der durch Stereotriangulation erhaltenen 3D-Punkte analysiert. Dies führt zu der Entdeckung eines systematischen Fehlers in der Schätzung der 3D-Position, der sich mit einer Neuformulierung der Projektionsgleichung korrigieren lässt. Die Simulationsergebnisse zeigen, dass eine bedeutende Verringerung des Fehlers in der geschätzten 3D-Punktposition möglich ist. Die Eigenbewegungsschätzung wird gewonnen, indem die Rotation und Translation zwischen Punktwolken geschätzt wird. Dieses Problem ist als „absolute Orientierung” bekannt und viele Lösungen auf Basis der Methode der kleinsten Quadrate sind in der Literatur vorgeschlagen worden. Diese Arbeit rezensiert die verfügbaren geschlossenen Lösungen zu dem Problem. Das vorgestellte System gliedert sich in drei wesentliche Bausteine: 1. Registrierung von Bildmerkmalen, 2. Eigenbewegungsschätzung und 3. iterative Schätzung von 3D-Position und 3D-Geschwindigkeit von Weltpunkten. Der erster Block erhält eine Folge rektifizierter Bilder als Eingabe und liefert daraus eine Liste von verfolgten Bildmerkmalen mit ihrer entsprechenden 3D-Position. Der Block „Eigenbewegungsschätzung” besteht aus vier Hauptschritten in einer Schleife: 1. Bewegungsvorhersage, 2. Anwendung der Glattheitsbedingung für die Bewegung (GBB), 3. absolute Orientierungsberechnung und 4. Bewegungsintegration. Die in dieser Dissertation vorgeschlagene GBB ist eine mächtige Bedingung für die Ablehnung von Ausreißern und für die Zuordnung von Gewichten zu den gemessenen 3D-Punkten. Simulationen werden mit gaußschem und slashschem Rauschen ausgeführt. Die Ergebnisse zeigen die Überlegenheit der GBB-Version über die Standardgewichtungsmethoden. Die Stabilität der Ergebnisse hinsichtlich Ausreißern wurde analysiert mit dem Resultat, dass der „break down point” größer als 50% ist. Wenn die vier Schritte iterativ ausgeführt, werden wird ein Prädiktor-Korrektor-Verfahren gewonnen.Wir nennen diese Schätzung Multi-frameschätzung im Gegensatz zur Zweiframeschätzung, die nur die aktuellen und vorherigen Bildpaare für die Berechnung der Eigenbewegung betrachtet. Die erste Iteration wird zwischen der aktuellen und vorherigen Wolke von Punkten durchgeführt. Jede weitere Iteration integriert eine zusätzliche Punktwolke eines vorherigen Zeitpunkts. Diese Methode reduziert die Fehlerakkumulation bei der Integration von mehreren Schätzungen in einer einzigen globalen Schätzung. Simulationsergebnisse zeigen, dass obwohl der Fehler noch superlinear im Laufe der Zeit zunimmt, die Größe des Fehlers um mehrere Größenordnungen reduziert wird. Der dritte Block besteht aus der iterativen Schätzung von 3D-Position und 3D-Geschwindigkeit von Weltpunkten. Hier wird eine Methode basierend auf einem Kalman Filter verwendet, das Stereo, Featuretracking und Eigenbewegungsdaten fusioniert. Messungen der Position eines Weltpunkts werden durch das Stereokamerasystem gewonnen. Die Differenzierung der Position des geschätzten Punkts erlaubt die zusätzliche Schätzung seiner Geschwindigkeit. Die Messungen werden durch das Messmodell gewonnen, das Stereo- und Bewegungsdaten fusioniert. Simulationsergebnisse validieren das Modell. Die Verringerung der Positionsunsicherheit im Laufe der Zeit wird mit einer Monte-Carlo Simulation erzielt. Experimentelle Ergebnisse werden mit langen Sequenzen von Bildern erzielt. Zusätzliche Tests, einschließlich einer 3D-Rekonstruktion einer Waldszene und der Berechnung der freien Kamerabewegung in einem Indoor-Szenario, wurden durchgeführt. Die Methode zeigt gute Ergebnisse in allen Fällen. Der Algorithmus liefert zudem akzeptable Ergebnisse bei der Schätzung der Lage kleiner Objekte, wie Köpfe und Beine von realen Crash-Test-Dummies