Mapping of Road Facilities and Information on High Definition Maps using Mobile Mapping System

学位の種別: 修士University of Tokyo(東京大学

Autonomous mobility scooters as assistive tools for the elderly

The aim of this research is to investigate the development of an autonomous navigation system that could be used as an assistive tool for elderly and disabled people in their activities of daily living. The navigation environment is an urban environment and the platform is a Mobility Scooter (MoS). To achieve this aim, a differentially steered MoS was modifed to receive motion commands from a computer and outfitted with onboard sensors that included a Global Positioning System (GPS) receiver and two 2D planar laser range sensors. Perception methods were developed to detect the presence of an outdoor pedestrian walkway. These methods achieved this by processing the range data produced by the laser sensors to identify features that are typically found around walkways like curbs, low vegetation, walls and barriers. A method that utilises GPS localisation information to plan and navigate a route in an outdoor urban environment was also developed. Extensive experimental work was conducted to test the accuracy, repeatability and usefulness of the sensory devices. The developed perception methodologies were evaluated in real world environments while the navigation algorithms were predominantly tested in virtual environments. A navigation system that plans a route in an urban environment and follows it using behaviours arranged in a hierarchy is presented and shown to have the ability to safely navigate an MoS along an outdoor pedestrian path

Semantische Segmentierung optischer Sensordaten für Anwendungen in der Binnenschifffahrt

Inland waterway transport (IWT) is an extremely important backbone for heavy good transportation with severe economical influence and the potential for the reduction oftraffic-related greenhouse gas emission. As IWT is expected to increase, updated chart data is required. Traditional survey methods are intense in cost and time. This work presents a processing scope for self-updating inland waterway charts. The required data can be gathered through optical sensors, that are fitted on IWT vessels. In semantic segmentation, every pixel in a RGB image is assigned to a defined class. This machine-learning problem is used to distinguish between various objects in a (IWT related) scene and thus to survey the infrastructure. For this task, the new BerlinIWT dataset is proposed. Existing datasets in this field may contain more examples, but do not provide an adequate number of classes. Training a neural network on the datasets MaSTr1325 and BerlinIWT leads to remarkable results. Spatial mapping information is completed with LiDAR (light detection and ranging) data. The acquired 3D point clouds provide precise distance information with a reasonable maximum range. The sensor compensates the flaws of (stereo) cameras, that are suitable for scene understanding, but inappropriate for distance measurements. The most suitable technique for the combination of LiDAR and camera data is discussed. For the ongoing scope towards simultaneous localisation and mapping (SLAM), two different methods for optical flow estimation are compared. Finally, further processing steps are pointed out and the application is discussed with respect to a traffic-telematics related use-case