Visual Mapping of the Ocean Floor
Traditionally, acoustical techniques have been used for mapping the ocean floor, e.g. using echo sounders. Our goal is to complement the acoustic information by establishing visual mapping techniques (and ultimately to combine the best of both worlds). They are directly understandable by human observers and they provide very high resolution 3D models that allow for measuring distances, surfaces, volumes etc. While e.g. multibeam echo sounders require external sensors for localization, the motion of a camera can be derived from the video sequence itself, which is called simultaneous localization and mapping (SLAM) or "structure and motion". Our goal is to map large deep sea environments with these methods in order to see what the status is and what is changing.One important and dynamic environment are black smoker fields. Here extremely hot water escapes from deep under the seafloor and the carried minerals create large structures, e.g. 20m high towers, in these environments. These black smokers are both biologically and geologically of very high interest and can also contain high amounts of resources. Understanding their growth and how the habitats around them behave is a central research question that can be tackled using visual methods and also visual-acoustic sensor fusion. During the research cruise Falkor-160320 that brought us close to Tonga, we have visually and acoustically scanned an old crater with 500m diameter that contains an entire black smoker field. The videos for this cruise can be seen here: From this, and also from other cruises, we have tremendeous amounts of video and photo material. Unfortunately, deep sea navigation data from external sensors is very inaccurate and so we have to use the visual data in order to refine the navigation. The general principle used is that corresponding seafloor points are seen and identified in several subsequent images, and also when the robot comes back to the same place. These correspondences provide geometric constraints on the camera motion, and thus on the robot motion. Once the motion is recovered, dense depth estimation techniques can be used to estimate the distance of each pixel in each image to the camera, to fuse these estimates and to finally create a 3D model of the environment. We are currently looking for students or HiWis to work on several sub-problems of this, see "Student Opportunities".
Modern photogrammetric methods like image mosaicking or 3D reconstruction allow to use images taken by optical cameras to accurately measure distances or volumes of objects in a scene of interest. In order to archive robust results, it is important to calibrate the geometric properties of the camera used. Technically, the corresponding 3D ray in space is determined for each pixel in the image. The DeepSea Monitoring group is interested in developing and experimenting with methods for refractive, underwater camera calibration. These approaches explicitly model light refraction at the underwater housing port, which is important for avoiding a systematic, geometric modeling error in later measurements. Based on such a calibration, it is possible to apply methods like refractive 3D reconstruction to images captured at the seafloor and compute a virtual, digital 3D model of the scene, that can be viewed interactively.For more details, procedures and tools on camera calibration, please visit our camera calibration page.
Size, number, rising speed and release frequency of bubbles are essential parameters for direct flux measurements of free gas release at the seafloor and the dissolution behaviour of gas bubbles in the water column. Such parameters are needed as input for hydroacoustic flux estimates and calculations for methane transport towards the sea surface. In this research, novel methods for visual bubble stream characterization are developed using a wide baseline stereo camera system that photographs rising bubbles at high frame rates in situ (the BubbleBox).
Because of the ever-changing requirements and missions at a research institute, GEOMAR needs flexible platforms and vehicles that are versatile, re-configurable and can be programmed to the respective needs. Additionally, such platforms should be controlled ideally on an abstract level. Desirable missions could be "to map a particular area and to come back with a map that contains no holes", "to explore some track and generate a summary of the different seafloor types", "to detect changes in a habitat by repeated scanning" or "to make detailed measurements once something unusual can be seen".For all these scenarios, the robot must process data onboard (real-time machine vision) and adapt the mission when needed.