Multimodal perception for autonomous driving

Mención Internacional en el título de doctorAutonomous driving is set to play an important role among intelligent transportation systems in the coming decades. The advantages of its large-scale implementation –reduced accidents, shorter commuting times, or higher fuel efficiency– have made its development a priority for academia and industry. However, there is still a long way to go to achieve full self-driving vehicles, capable of dealing with any scenario without human intervention. To this end, advances in control, navigation and, especially, environment perception technologies are yet required. In particular, the detection of other road users that may interfere with the vehicle’s trajectory is a key element, since it allows to model the current traffic situation and, thus, to make decisions accordingly. The objective of this thesis is to provide solutions to some of the main challenges of on-board perception systems, such as extrinsic calibration of sensors, object detection, and deployment on real platforms. First, a calibration method for obtaining the relative transformation between pairs of sensors is introduced, eliminating the complex manual adjustment of these parameters. The algorithm makes use of an original calibration pattern and supports LiDARs, and monocular and stereo cameras. Second, different deep learning models for 3D object detection using LiDAR data in its bird’s eye view projection are presented. Through a novel encoding, the use of architectures tailored to image detection is proposed to process the 3D information of point clouds in real time. Furthermore, the effectiveness of using this projection together with image features is analyzed. Finally, a method to mitigate the accuracy drop of LiDARbased detection networks when deployed in ad-hoc configurations is introduced. For this purpose, the simulation of virtual signals mimicking the specifications of the desired real device is used to generate new annotated datasets that can be used to train the models. The performance of the proposed methods is evaluated against other existing alternatives using reference benchmarks in the field of computer vision (KITTI and nuScenes) and through experiments in open traffic with an automated vehicle. The results obtained demonstrate the relevance of the presented work and its suitability for commercial use.La conducción autónoma está llamada a jugar un papel importante en los sistemas inteligentes de transporte de las próximas décadas. Las ventajas de su implementación a larga escala –disminución de accidentes, reducción del tiempo de trayecto, u optimización del consumo– han convertido su desarrollo en una prioridad para la academia y la industria. Sin embargo, todavía hay un largo camino por delante hasta alcanzar una automatización total, capaz de enfrentarse a cualquier escenario sin intervención humana. Para ello, aún se requieren avances en las tecnologías de control, navegación y, especialmente, percepción del entorno. Concretamente, la detección de otros usuarios de la carretera que puedan interferir en la trayectoria del vehículo es una pieza fundamental para conseguirlo, puesto que permite modelar el estado actual del tráfico y tomar decisiones en consecuencia. El objetivo de esta tesis es aportar soluciones a algunos de los principales retos de los sistemas de percepción embarcados, como la calibración extrínseca de los sensores, la detección de objetos, y su despliegue en plataformas reales. En primer lugar, se introduce un método para la obtención de la transformación relativa entre pares de sensores, eliminando el complejo ajuste manual de estos parámetros. El algoritmo hace uso de un patrón de calibración propio y da soporte a cámaras monoculares, estéreo, y LiDAR. En segundo lugar, se presentan diferentes modelos de aprendizaje profundo para la detección de objectos en 3D utilizando datos de escáneres LiDAR en su proyección en vista de pájaro. A través de una nueva codificación, se propone la utilización de arquitecturas de detección en imagen para procesar en tiempo real la información tridimensional de las nubes de puntos. Además, se analiza la efectividad del uso de esta proyección junto con características procedentes de imágenes. Por último, se introduce un método para mitigar la pérdida de precisión de las redes de detección basadas en LiDAR cuando son desplegadas en configuraciones ad-hoc. Para ello, se plantea la simulación de señales virtuales con las características del modelo real que se quiere utilizar, generando así nuevos conjuntos anotados para entrenar los modelos. El rendimiento de los métodos propuestos es evaluado frente a otras alternativas existentes haciendo uso de bases de datos de referencia en el campo de la visión por computador (KITTI y nuScenes), y mediante experimentos en tráfico abierto empleando un vehículo automatizado. Los resultados obtenidos demuestran la relevancia de los trabajos presentados y su viabilidad para un uso comercial.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Jesús García Herrero.- Secretario: Ignacio Parra Alonso.- Vocal: Gustavo Adolfo Peláez Coronad

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality