No abstract

Efficient registration of point clouds from terrestrial laser scanners enables us to move from scanning to point cloud applications immediately. In this paper, a new efficient rough registration method of laser-scanned point clouds of bridges is proposed. Our method relies on straight-line edges as linear features, which often appear in many bridges. Efficient edge-line extraction and line-based registration methods are described in this paper. In our method, first, sampled regular point clouds based on the azimuth and elevation angles are created, and planar regions are extracted using the region growing on the regular point clouds. Then, straight lines of the edges of the planar regions are extracted as linear features. Next, vertical and horizontal line clusters are created according to the direction of the lines. To align the position and orientation of two point clouds, two corresponding nonparallel line pairs from line clusters are used. In the registration process, the RANSAC approach with a hash table of line pairs is used. In this process, the hash table is used for finding candidates of corresponding line pairs efficiently. Sampled points on the line pairs are used to align the line pairs, and occupied voxels and downsampled point clouds are used for efficient consensus calculation. The method is tested using three data sets of different types of bridges: a small steel bridge, a middle-size concrete bridge, and a high-pier concrete bridge. In our experiments, successful rates of our rough registration were 100%, and the processing time of rough registration for 19 point clouds was about 1 min.

We developed an embedded vision system that can accelerate the basic image processing functions for mobile robot navigation with compact hardware featuring low power consumption. The system is composed of a Digital Signal Processor (DSP) and a dedicated LSI for low-level image processing, specifically for spatial filtering, feature extraction, and block matching operations. The image processing LSI has a dedicated systolic array processor consisting of 64 processing elements to accelerate basic block operations for image feature calculation and correlation-based image matching. The power consumption is only 10 W, about one-seventh that of a typical Pentium 4 personal computer, but the processing speed for correlation matching is roughly three times faster than such a system. With this vision system, we implemented a stereo-visionbased navigation algorithm on our mobile service robot and performed a visual navigation experiment in a building hallway.

Recently, many countries have faced serious problems associated with aging civil infrastructures such as bridges, tunnels, dams, highways and so on. Aging infrastructures are increasing year by year and suitable maintenance actions are necessary to maintain their safety and serviceability. In fact, infrastructure deterioration has caused serious problems in the past. In order to prevent accidents with civil infrastructures, supervisors must spend a lot of money to maintain the safe conditions of infrastructures. Therefore, new technologies are required to reduce maintenance costs. In 2014 the Japanese government started the Cross-Ministerial Strategic Innovation Promotion Program (SIP), and technologies for infrastructure maintenance have been studied in the SIP project [1]. Fujitsu Limited, Hokkaido University, The University of Tokyo, Nagoya Institute of Technology and Docon Co. Limited have been engaged in the SIP project to develop a bridge inspection support system using information technology and robotic technology. Our system is divided into the following two main parts: bridge inspection support robots using a two-wheeled multicopter, and an inspection data management system utilizing 3D modeling technology. In this paper, we report the bridge inspection support system developed in our SIP project.

We developed a bridge inspection support robot system that uses a two-wheeled multicopter and 3D modeling technology. Our system is divided into three main parts: 1) a two-wheeled multicopter to capture close-up images of the inspection target, 2) damage extraction using 3D modeling technology, and 3) a 3D model-based bridge maintenance system that stores inspection information linked with the Industry Foundation Classes (IFC) 3D product model. An inspector can use this system to visually inspect the overall structure and accurately detect hairline cracks.