Rui-Jun Yan

Representation of 3D Environment Map Using B-Spline Surface with Two Mutually Perpendicular LRFs

This paper proposes a map representation method of three-dimensional (3D) environment by using B-spline surfaces, which are first used to describe large environment in 3D map construction research. Initially, a 3D point cloud map is constructed based on extracted line segments with two mutually perpendicular 2D laser range finders (LRFs). Then two types of accumulated data sets are separated from the point cloud map according to different types of robot movements, continuous translation and continuous rotation. To express the environment more accurately, B-spline surface with covariance matrix is proposed to be extracted from each data set. Due to the random movements, there must be overlap between extracted B-spline surfaces. However, merging of two overlapping B-spline surfaces with different distribution directions of their control points is a complex problem, which is not well addressed by far. In our proposed method, each surface is divided into overlap and nonoverlap. Then generated sample points with propagated uncertainties from one overlap and their projection points located on the other overlap are merged using the product of Gaussian probability density functions. Based on this merged data set, a new surface is extracted to represent the environment instead of the two overlaps. Finally, proposed methods are validated by using the experimental result of an accurate representation of an indoor environment with B-spline surfaces.

Extraction of Different Types of Geometrical Features from Raw Sensor Data of Two-dimensional LRF

This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.

Development of a novel post-construction quality assessment robot system

Utilizing construction quality standards for almost perfect building projects ensures future marketability of projects, customer satisfaction and maximization of asset value. This paper describes a novel robot system to autonomously assess the post-construction quality of buildings which is currently done manually by using human inspectors. However, manual inspection always has the disadvantages, such as labile inspection accuracy, being time consuming and indistinct recording. As a novel solution to the aforementioned drawbacks, a mobile robot is equipped with a laser scanner, a thermal camera, an inclinometer and a RGB camera to achieve an autonomous assessment system. This proposed system can assess five types of defects: evenness, alignment, cracks, hollowness, and inclination. A movable trolley with different mechanisms are designed to mount and integrate all these sensors. Its mechanical design with four motors and one linear actuator, which are installed to increase the measurement range of sensors, is also presented. The experimental tests show that the proposed system has a great potential in construction quality assessment area in building sector.

A novel method for 3D reconstruction: Division and merging of overlapping B-spline surfaces ☆

Abstract B-spline surfaces, extracted from scanned sensor data, are usually required to represent objects in inspection, surveying technology, metrology and reverse engineering tasks. In order to express a large object with a satisfactory accuracy, multiple scans, which generally lead to overlapping patches, are always needed due to, inter-alia, practical limitations and accuracy of measurements, uncertainties in measurement devices, calibration problems as well as skills of the experimenter. In this paper, we propose an action sequence consisting of division and merging. While the former divides a B-spline surface into many patches with corresponding scanned data, the latter merges the scanned data and its overlapping B-spline surface patch. Firstly, all possible overlapping cases of two B-spline surfaces are enumerated and analyzed from a view of the locations of the projection points of four corners of one surface in the interior of its overlapping surface. Next, the general division and merging methods are developed to deal with all overlapping cases, and a simulated example is used to illustrate aforementioned detailed procedures. In the sequel, two scans obtained from a three-dimensional laser scanner are simulated to express a large house with B-spline surfaces. The simulation results show the efficiency and efficacy of the proposed method. In this whole process, storage space of data points is not increased with new obtained overlapping scans, and none of the overlapping points are discarded which increases the representation accuracy. We believe the proposed method has a number of potential applications in the representation and expression of large objects with three-dimensional laser scanner data.

Arc/Line Segments-based SLAM by Updating Accumulated Sensor Data

This paper presents arc/line segments-based Simultaneous Localization and Mapping (SLAM) by updating accumulated laser sensor data with a mobile robot moving in an unknown environment. For each scan, the sensor data in the set are stored by a small constant number of parameters that can recover the necessary information contained in the raw data of the group. The arc and line segments are then extracted according to different limit values, but based on the same parameters. If two segments, whether they are homogenous features or not, from two scans are matched successfully, the new segment is extracted from the union set with combined data information obtained by means of summing the equivalent parameters of these two sets, not combining the features directly. The covariance matrixes of the segments are also updated and calculated synchronously employing the same parameters. The experiment results obtained in an irregular indoor environment show the good performance of the proposed method.

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.

Innovations in Infrastructure Service Robots

Infrastructure service robotics is a discipline studying robotic systems and methodology for buildings and civil infrastructure construction, inspection, and maintenance. The target could be buildings, estates, parks, bridges, power plants, power transmission lines, underground tunnels, sewage pipes, port facilities, etc. In this article, several new infrastructure service robots projects for construction services and deep tunnel inspection carried out in Singapore will be introduced. With new actuators, low cost sensors, and open source robotics software, infrastructure robots represent a new breed of intelligent systems that help the society to overcome manpower shortage and ageing workforce. These projects are examples of user-led and user-inspired robotics R&D effort led by government agencies, universities, and industrial alliance of local and overseas robotics and construction machinery manufacturers, start-up companies, and system integrators. The ultimate goal is to strengthen the robotics R&D capability in Singapore and to foster a robotics industry and the ecosystem that transform Singapore into a Smart Nation.