Method of collaborative detection of autonomous transport vehicles based on laser rangefinder data

To master changing performance demands, autonomous transport vehicles are deployed to make inhouse material flow applications more flexible. The socalled cellular transport system consists of a multitude of small scale transport vehicles which shall be able to form a swarm. Therefore the vehicles need to detect each other, exchange information amongst each other and sense their environment. By provision of peripherally acquired information of other transport entities, more convenient decisions can be made in terms of navigation and collision avoidance. This paper is a contribution to collective utilization of sensor data in the swarm of cellular transport vehicles.

(Supplemet S1) Silicon oxide and Potassium + Sodium oxide from the PACManus hydrothermal area

This study presents a systematic analysis and interpretation of autonomous underwater vehicle-based microbathymetry combined with remotely operated vehicle (ROV) video recordings, rock analyses and temperaturemeasurements within the PACManus hydrothermal area located on Pual Ridge in the Bismarck Sea of eastern Manus Basin. The data obtained during research cruise Magellan-06 and So-216 provides a framework for understanding the relationship between the volcanism, tectonismand hydrothermal activity. PACManus is a submarine felsic vocanically-hosted hydrothermal area that hosts multiple vent fields locatedwithin several hundredmeters of one another but with different fluid chemistries, vent temperatures and morphologies. The total area of hydrothermal activity is estimated to be 20,279m**2. Themicrobathymetrymaps combinedwith the ROV video observations allow for precise high-resolution mapping estimates of the areal extents of hydrothermal activity.We find the distribution of hydrothermal fields in the PACManus area is primarily controlled by volcanic features that include lava domes, thick andmassive blocky lava flows, breccias and feeder dykes. Spatial variation in the permeability of local volcanic facies appears to control the distribution of venting within a field.We define a three-stage chronological sequence for the volcanic evolution of the PACManus based on lava flow morphology, sediment cover and lava SiO2 concentration. In Stage-1, sparsely to moderately porphyritic dacite lavas (68-69.8 wt.% SiO2) erupted to form domes or cryptodomes. In Stage-2, aphyric lava with slightly lower SiO2 concentrations (67.2-67.9 wt.% SiO2) formed jumbled and pillowed lava flows. In the most recent phase Stage-3, massive blocky lavaswith 69 to 72.5wt.% SiO2were erupted throughmultiple vents constructing a volcanic ridge identified as the PACManus neovolcanic zone. The transition between these stages may be gradual and related to progressive heating of a silicic magma following a recharge event of hot, mantle-derived melts.

Profile of sediment echo sounding with links to ParaSound data files and swath sonar multibeam bathymetry during METEOR cruise M70/2b

Two highly active mud volcanoes located in 990-1,265 m water depths were mapped on the northern Egyptian continental slope during the BIONIL expedition of R/V Meteor in October 2006. High-resolution swath bathymetry and backscatter imagery were acquired with an autonomous underwater vehicle (AUV)-mounted multibeam echosounder, operating at a frequency of 200 kHz. Data allowed for the construction of ~1 m pixel bathymetry and backscatter maps. The newly produced maps provide details of the seabed morphology and texture, and insights into the formation of the two mud volcanoes. They also contain key indicators on the distribution of seepage and its tectonic control. The acquisition of high-resolution seafloor bathymetry and acoustic imagery maps with an AUV-mounted multibeam echosounder fills the gap in spatial scale between conventional multibeam data collected from a surface vessel and in situ video observations made from a manned submersible or a remotely operating vehicle.

Gridded bathymetry from multibeam echosounder EM122 data of the cruise M84/2, off Eregli (2011)

We investigated gas bubble emissions at the Don-Kuban paleo-fan in the northeastern Black Sea regarding their geological setting, quantities as well as spatial and temporal variabilities during three ship expeditions between 2007 and 2011. About 600 bubble-induced hydroacoustic anomalies in the water column (flares) originating from the seafloor above the gas hydrate stability zone (GHSZ) at ~700 m water depth were found. At about 890 m water depth a hydrocarbon seep area named "Kerch seep area" was newly discovered within the GHSZ. We propose locally domed sediments ('mounds') discovered during ultra-high resolution bathymetric mapping with an autonomous underwater vehicle (AUV) to result from gas hydrate accumulation at shallow depths. In situ measurements indicated spatially limited temperature elevations in the shallow sediment likely induced by upward fluid flow which may confine the local GHSZ to a few meters below the seafloor. As a result, gas bubbles are suspected to migrate into near-surface sediments and to escape the seafloor through small-scale faults. Hydroacoustic surveys revealed that several flares originated from a seafloor area of about 1 km**2 in size. The highest flare disappeared in about 350 m water depth, suggesting that the released methane remains in the water column. A methane flux estimate, combining data from visual quantifications during dives with a remotely operated vehicle (ROV) with results from ship-based hydroacoustic surveys and gas analysis revealed that between 2 and 87 x 10**6 mol CH4 yr-1 escaped into the water column above the Kerch seep area. Our results show that the finding of the Kerch seep area represents a so far underestimated type of hydrocarbon seep, which has to be considered in methane budget calculations.

Dissipation rate measurements during R/V Maria S. Merian cruise MSM21/1b dives

GEOMAR's autonomous underwater vehicle (AUV Abyss REMUS 6000) was deployed within the framework of a multi-platform experiment in June 2012 with R/V Maria S. Merian cruise MSM21/1b at about 180 km downstream of Denmark Strait. The scientific payload included a pumped Seabird 49 FastCAT CTD system, a paroscientific pressure sensor, and shear and temperature microstructure profiler from Rockland Scientific Inc.. In total, six of eight AUV dives were carried out successfully. Aborts on three dives were caused by strong counter currents the AUV experienced in the Denmark Strait Overflow plume, which made the AUV fail to reach its waypoints on schedule. During all missions the AUV was programmed to dive at constant depth levels along? straight legs approximately parallel to chosen isobaths with a constant speed of 1.6 m s-1 through the water.

Physical oceanography from R/V Maria S. Merian cruise MSM21/1b dives

GEOMAR's autonomous underwater vehicle (AUV Abyss REMUS 6000) was deployed within the framework of a multi-platform experiment in June 2012 with R/V Maria S. Merian cruise MSM21/1b at about 180 km downstream of Denmark Strait. The scientific payload included a pumped Seabird 49 FastCAT CTD system, a paroscientific pressure sensor, and shear and temperature microstructure profiler from Rockland Scientific Inc.. In total, six of eight AUV dives were carried out successfully. Aborts on three dives were caused by strong counter currents the AUV experienced in the Denmark Strait Overflow plume, which made the AUV fail to reach its waypoints on schedule. During all missions the AUV was programmed to dive at constant depth levels along? straight legs approximately parallel to chosen isobaths with a constant speed of 1.6 m s-1 through the water.

Physical oceanography, current meter data, and microstructure measurements 180 km downstream of Denmark Strait sill during Maria S. Merian cruise MSM21/1b

The main objectives of the cruise MSM21/1b were to characterize the spatio-temporal variability of the Denmark Strait overflow and to identify processes responsible for the exchange of the overflow plume with ambient water downstream of Denmark Strait. A multi-platform approach was taken to achieve the goals, based on moorings, an autonomous underwater vehicle (AUV), as well as lowered and vessel-mounted observations. From these platforms, measurements of temperature, salinity, dissolved oxygen, current velocity, dissipation of turbulent kinetic energy, and bottom pressure were obtained.

Uncalibrated temperature measurement during R/V Maria S. Merian cruise MSM21/1b

GEOMAR's autonomous underwater vehicle (AUV Abyss REMUS 6000) was deployed within the framework of a multi-platform experiment in June 2012 with R/V Maria S. Merian cruise MSM21/1b at about 180 km downstream of Denmark Strait. The scientific payload included a pumped Seabird 49 FastCAT CTD system, a paroscientific pressure sensor, and shear and temperature microstructure profiler from Rockland Scientific Inc.. In total, six of eight AUV dives were carried out successfully. Aborts on three dives were caused by strong counter currents the AUV experienced in the Denmark Strait Overflow plume, which made the AUV fail to reach its waypoints on schedule. During all missions the AUV was programmed to dive at constant depth levels along? straight legs approximately parallel to chosen isobaths with a constant speed of 1.6 m s-1 through the water.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Cartography for cooperative manoeuvres with autonomous land vehicles

This article presents a cartographic system to facilitate cooperative manoeuvres among autonomous vehicles in a well-known environment. The main objective is to design an extended cartographic system to help in the navigation of autonomous vehicles. This system has to allow the vehicles not only to access the reference points needed for navigation, but also noticeable information such as the location and type of traffic signals, the proximity to a crossing, the streets en route, etc. To do this, a hierarchical representation of the information has been chosen, where the information has been stored in two levels. The lower level contains the archives with the Universal Traverse Mercator (UTM) coordinates of the points that define the reference segments to follow. The upper level contains a directed graph with the relational database in which streets, crossings, roundabouts and other points of interest are represented. Using this new system it is possible to know when the vehicle approaches a crossing, what other paths arrive at that crossing, and, should there be other vehicles circulating on those paths and arriving at the crossing, which one has the highest priority. The data obtained from the cartographic system is used by the autonomous vehicles for cooperative manoeuvres.

Intelligent automatic overtaking system using vision for vehicle detection

There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manœuvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle’s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroën car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles – a motorbike, car, and truck – with encouraging results.

Evaluation of mission planning strategies of a robotic aerial vehicle for the AIRC international competition

The International Aerial Robotics Competition (IARC) is an important event where teams from universities design flying autonomous vehicles to overcome the last challenges in the field. The goal of the Seventh Mission proposed by the IARC is to guide several mobile ground robots to a target area. The scenario is complex and not determinist due to the random behavior of the ground robots movement. The UAV must select efficient strategies to complete the mission. The goal of this work has been evaluating different alternative mission planning strategies of a UAV for this competition. The Mission Planner component is in charge of taking the UAV decisions. Different strategies have been developed and evaluated for the component, achieving a better performance Mission Planner and valuable knowledge about the mission. For this purpose, it was necessary to develop a simulator to evaluate the different strategies. The simulator was built as an improvement of an existing previous version.

A Vision-based Quadrotor Multi-robot Solution for the Indoor Autonomy Challenge of the 2013 International Micro Air Vehicle Competition

This paper presents a completely autonomous solution to participate in the Indoor Challenge of the 2013 International Micro Air Vehicle Competition (IMAV 2013). Our proposal is a multi-robot system with no centralized coordination whose robotic agents share their position estimates. The capability of each agent to navigate avoiding collisions is a consequence of the resulting emergent behavior. Each agent consists of a ground station running an instance of the proposed architecture that communicates over WiFi with an AR Drone 2.0 quadrotor. Visual markers are employed to sense and map obstacles and to improve the pose estimation based on Inertial Measurement Unit (IMU) and ground optical flow data. Based on our architecture, each robotic agent can navigate avoiding obstacles and other members of the multi-robot system. The solution is demonstrated and the achieved navigation performance is evaluated by means of experimental flights. This work also analyzes the capabilities of the presented solution in simulated flights of the IMAV 2013 Indoor Challenge. The performance of the CVG UPM team was awarded with the First Prize in the Indoor Autonomy Challenge of the IMAV 2013 competition.