Robust 3D IMU-LIDAR Calibration and Multi Sensor Probabilistic State Estimation

Autonomous robots are highly complex systems. In order to operate in dynamic environments, adaptability in their decision-making algorithms is a must. Thus, the internal and external information that robots obtain from sensors is critical to re-evaluate their decisions in real time. Accuracy is key in this endeavor, both from the hardware side and the modeling point of view. In order to guarantee the highest performance, sensors need to be correctly calibrated. To this end, some parameters are tuned so that the particular realization of a sensor best matches a generalized mathematical model. This step grows in complexity with the integration of multiple sensors, which is generally a requirement in order to cope with the dynamic nature of real world applications. This project aims to deal with the calibration of an inertial measurement unit, or IMU, and a Light Detection and Ranging device, or LiDAR. An offline batch optimization procedure is proposed to optimally estimate the intrinsic and extrinsic parameters of the model. Then, an online state estimation module that makes use of the aforementioned parameters and the fusion of LiDAR-inertial data for local navigation is proposed. Additionally, it incorporates real time corrections to account for the time-varying nature of the model, essential to deal with exposure to continued operation and wear and tear. Keywords: sensor fusion, multi-sensor calibration, factor graphs, batch optimization, Gaussian Processes, state estimation, LiDAR-inertial odometry, Error State Kalman Filter, Normal Distributions Transform

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

Comparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments

Global navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous driving. In recent years, exteroceptive sensors have gained much attention due to significant improvements in accuracy and cost-effectiveness, especially for 3D range sensors. By registering two successive 3D scans, known as scan matching, it is possible to estimate the pose of a vehicle. This work aims to provide in-depth analysis and comparison of the state-of-the-art 3D scan matching approaches as a solution to the localization problem of autonomous vehicles. Eight techniques (deterministic and probabilistic) are investigated: iterative closest point (with three different embodiments), normal distribution transform, coherent point drift, Gaussian mixture model, support vector-parametrized Gaussian mixture and the particle filter implementation. They are demonstrated in long path trials in both urban and agricultural environments and compared in terms of accuracy and consistency. On the one hand, most of the techniques can be successfully used in urban scenarios with the probabilistic approaches that show the best accuracy. On the other hand, agricultural settings have proved to be more challenging with significant errors even in short distance trials due to the presence of featureless natural objects. The results and discussion of this work will provide a guide for selecting the most suitable method and will encourage building of improvements on the identified limitations.This project has been supported by the National Agency of Research and Development (ANID, ex-Conicyt) under Fondecyt grant 1201319, Basal grant FB0008, DGIIP-UTFSM Chile, National Agency for Research and Development (ANID)/PCHA/Doctorado Nacional/2020-21200700, Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Span ish Ministry of Science, Innovation and Universities (project RTI2018- 094222-B-I00) for partially funding this research. The Spanish Ministry of Education is thanked for Mr. J. Gene’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisitio

Exploiting Radio Fingerprints for Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles using radio fingerprints, for example WiFi (Wireless Fidelity) or LTE (Long Term Evolution) radio features, which are widely available in the existing infrastructure. In particular, we present two solutions to exploit the radio fingerprints for SLAM. In the first solution-namely Radio SLAM, the output is a radio fingerprint map generated using SLAM technique. In the second solution-namely Radio+LiDAR SLAM, we use radio fingerprint to assist conventional LiDAR-based SLAM to improve accuracy and speed, while generating the occupancy map. We demonstrate the effectiveness of our system in three different environments, namely outdoor, indoor building, and semi-indoor environment.Comment: This paper has been accepted by IEEE Pervasive Computing with DOI: 10.1109/MPRV.2023.327477

Ambient-Aware LiDAR Odometry in Variable Terrains

The flexibility of Simultaneous Localization and Mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odometry drift in high-noise settings, integrating clustering methods to filter out unstable features has become an effective module of SLAM frameworks. However, reducing the amount of point cloud data can lead to potential loss of information and possible degeneration. As a result, this research proposes a LiDAR odometry that can dynamically assess the point cloud's reliability. The algorithm aims to improve adaptability in diverse settings by selecting important feature points with sensitivity to the level of environmental degeneration. Firstly, a fast adaptive Euclidean clustering algorithm based on range image is proposed, which, combined with depth clustering, extracts the primary structural points of the environment defined as ambient skeleton points. Then, the environmental degeneration level is computed through the dense normal features of the skeleton points, and the point cloud cleaning is dynamically adjusted accordingly. The algorithm is validated on the KITTI benchmark and real environments, demonstrating higher accuracy and robustness in different environments

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Reliable localization methods for intelligent vehicles based on environment perception

Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport Systems (ITS) as a potential future of transportation. Today, thanks to all the technological advances in recent years, the feasibility of such systems is no longer a question. Some of these autonomous driving technologies are already sharing our roads, and even commercial vehicles are including more Advanced Driver-Assistance Systems (ADAS) over the years. As a result, transportation is becoming more efficient and the roads are considerably safer. One of the fundamental pillars of an autonomous system is self-localization. An accurate and reliable estimation of the vehicle’s pose in the world is essential to navigation. Within the context of outdoor vehicles, the Global Navigation Satellite System (GNSS) is the predominant localization system. However, these systems are far from perfect, and their performance is degraded in environments with limited satellite visibility. Additionally, their dependence on the environment can make them unreliable if it were to change. Accordingly, the goal of this thesis is to exploit the perception of the environment to enhance localization systems in intelligent vehicles, with special attention to their reliability. To this end, this thesis presents several contributions: First, a study on exploiting 3D semantic information in LiDAR odometry is presented, providing interesting insights regarding the contribution to the odometry output of each type of element in the scene. The experimental results have been obtained using a public dataset and validated on a real-world platform. Second, a method to estimate the localization error using landmark detections is proposed, which is later on exploited by a landmark placement optimization algorithm. This method, which has been validated in a simulation environment, is able to determine a set of landmarks so the localization error never exceeds a predefined limit. Finally, a cooperative localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle detections in order to enhance the estimation provided by GNSS systems. Multiple experiments are carried out in different simulation environments to validate the proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes del Transporte (ITS) se veían como un futuro para el transporte con gran potencial. Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada vez más eficiente y las carreteras son considerablemente más seguras. Uno de los pilares fundamentales de un sistema autónomo es la autolocalización. Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial para la navegación. En el contexto de los vehículos circulando en exteriores, el Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante. Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los cambios en el entorno pueden provocar cambios en la estimación, lo que los hace poco fiables en ciertas situaciones. Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar los sistemas de localización en vehículos inteligentes, con una especial atención a la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones: En primer lugar, se presenta un estudio sobre cómo aprovechar la información semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la contribución de cada tipo de elemento del entorno a la salida de la odometría. Los resultados experimentales se han obtenido utilizando una base de datos pública y se han validado en una plataforma de conducción del mundo real. En segundo lugar, se propone un método para estimar el error de localización utilizando detecciones de puntos de referencia, que posteriormente es explotado por un algoritmo de optimización de posicionamiento de puntos de referencia. Este método, que ha sido validado en un entorno de simulación, es capaz de determinar un conjunto de puntos de referencia para el cual el error de localización nunca supere un límite previamente fijado. Por último, se propone un algoritmo de localización cooperativa basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método propuesto ha sido validado mediante múltiples experimentos en diferentes entornos de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño