Visual 3-D SLAM from UAVs

The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs

F1/10: An Open-Source Autonomous Cyber-Physical Platform

In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of safety and performance on real vehicles is costly and hazardous. The use of such vehicles is also outside the reach of most researchers and students. In this paper, we present F1/10: an open-source, affordable, and high-performance 1/10 scale autonomous vehicle testbed. The F1/10 testbed carries a full suite of sensors, perception, planning, control, and networking software stacks that are similar to full scale solutions. We demonstrate key examples of the research enabled by the F1/10 testbed, and how the platform can be used to augment research and education in autonomous systems, making autonomy more accessible

Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio

A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots

Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehículos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una línea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo así obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehículos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehículos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃa˛ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altímetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capítulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, así como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, así como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad científica

A distributed architecture for unmanned aerial systems based on publish/subscribe messaging and simultaneous localisation and mapping (SLAM) testbed

A dissertation submitted in fulfilment for the degree of Master of Science. School of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa, November 2017The increased capabilities and lower cost of Micro Aerial Vehicles (MAVs) unveil big opportunities for a rapidly growing number of civilian and commercial applications. Some missions require direct control using a receiver in a point-to-point connection, involving one or very few MAVs. An alternative class of mission is remotely controlled, with the control of the drone automated to a certain extent using mission planning software and autopilot systems. For most emerging missions, there is a need for more autonomous, cooperative control of MAVs, as well as more complex data processing from sensors like cameras and laser scanners. In the last decade, this has given rise to an extensive research from both academia and industry. This research direction applies robotics and computer vision concepts to Unmanned Aerial Systems (UASs). However, UASs are often designed for specific hardware and software, thus providing limited integration, interoperability and re-usability across different missions. In addition, there are numerous open issues related to UAS command, control and communication(C3), and multi-MAVs. We argue and elaborate throughout this dissertation that some of the recent standardbased publish/subscribe communication protocols can solve many of these challenges and meet the non-functional requirements of MAV robotics applications. This dissertation assesses the MQTT, DDS and TCPROS protocols in a distributed architecture of a UAS control system and Ground Control Station software. While TCPROS has been the leading robotics communication transport for ROS applications, MQTT and DDS are lightweight enough to be used for data exchange between distributed systems of aerial robots. Furthermore, MQTT and DDS are based on industry standards to foster communication interoperability of “things”. Both protocols have been extensively presented to address many of today’s needs related to networks based on the internet of things (IoT). For example, MQTT has been used to exchange data with space probes, whereas DDS was employed for aerospace defence and applications of smart cities. We designed and implemented a distributed UAS architecture based on each publish/subscribe protocol TCPROS, MQTT and DDS. The proposed communication systems were tested with a vision-based Simultaneous Localisation and Mapping (SLAM) system involving three Parrot AR Drone2 MAVs. Within the context of this study, MQTT and DDS messaging frameworks serve the purpose of abstracting UAS complexity and heterogeneity. Additionally, these protocols are expected to provide low-latency communication and scale up to meet the requirements of real-time remote sensing applications. The most important contribution of this work is the implementation of a complete distributed communication architecture for multi-MAVs. Furthermore, we assess the viability of this architecture and benchmark the performance of the protocols in relation to an autonomous quadcopter navigation testbed composed of a SLAM algorithm, an extended Kalman filter and a PID controller.XL201

Range-only SLAM with a mobile robot and a Wireless Sensor Networks

This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of a Wireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the transmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks

Implementing and Tuning an Autonomous Racing Car Testbed

Achieving safe autonomous driving is far from a vision at present days, with many examples like Uber, Google and the most famous of all Tesla, as they successfully deployed self driving cars around the world. Researchers and engineers have been putting tremendous efforts and will continue to do so in the following years into developing safe and precise control algorithms and technologies that will be included in future self driving cars. Besides these well known autonomous car deployments, some focus has also been put into autonomous racing competitions, for example the Roborace. The fact is that although significant progress that has been made, testing on real size cars in real environments requires immense financial support, making it impossible for many research groups to enter the game. Consequently, interesting alternatives appeared, such as the F1 Tenth, which challenges students, researchers and engineers to embrace in a low cost autonomous racing competition while developing control algorithms, that rely on sensors and strategies used in real life applications. This thesis focus on the comparison of different control algorithms and their effectiveness, that are present in a racing aspect of the F1 Tenth competition. In this thesis, efforts were put into developing a robotic autonomous car, relying on Robot Operative System, ROS, that not only meet the specifications from the F1 Tenth rules, but also allowed to establish a testbed for different future autonomous driving research.Obter uma condução autónoma segura está longe de uma visão dos dias de hoje, com exemplos como a Uber, Google e o mais famoso deles todos, a Tesla, que já foram globalmente introduzidos com sucesso. Investigadores e engenheiros têm colocado um empenho tremendo e vão continuar a fazê-lo nos próximos anos, a desenvolver algoritmos de controlo precisos e seguros, bem como tecnologias que serão colocados nos carros autónomos do futuro. Para além destes casos de sucesso bem conhecidos, algum foco tem sido colocado em competições de corridas de carros autónomos, como por exemplo o Roborace. O facto ´e que apesar do progresso significante que tem sido feito, fazer testes em carros reais em cenários verdadeiros, requer grande investimento financeiro, tornando impossível para muitos grupos de investigação investir na área. Consequentemente, apareceram alternativas relevantes, tal como o F1 Tenth, que desafia estudantes, investigadores e engenheiros a aderir a uma competição de baixos custos de corridas autónomas, enquanto desenvolvem algoritmos de controlo, que dependem de sensores e estratégias usadas em aplicações reais. Esta tese foca-se na comparação de diferentes algoritmos de controlo e na eficácia dos mesmos, que estão presentes num cenário de corrida da competição do F1 Tenth. Nesta tese, foram colocados muitos esforços para o desenvolvimento de um carro autónomo robótico, baseado em Robot Operative System, ROS, que não só vai de encontro `as especificações do F1 Tenth, mas que também permita estabelecer uma plataforma para futuras investigações de condução autónoma