Automatic subsurface mapping is essential in the construction services, as it is anticipated to become the main operational environment of the future robots to be realized in the respective domain. Towards this direction, the paper at hand, introduces for the first time herein, an integrated framework for subsurface mapping by exploiting a surface operating mobile robot with a Ground Penetrating Radar (GPR). The mobile robot tows the GPR antenna, which is mounted on a specifically designed trailer, and is utilized as the mean to cover the surface area, while at the same time the antenna scans the subsurface by emitting electromagnetic pulses. The gathered data are processed for the construction of a subsurface 3D map. Specifically, image processing techniques, that involve background segmentation, HOG [1] feature extraction, hypothesis verification and matching are applied on the 2D radargram (B-Scan) for the detection of the salient points that correspond to buried utilities. By employing the pulse propagation velocity into the subsurface and the soil utilities, the salient points are expressed in world coordinates and used for the composition of the 3D subsurface map. Our method has been evaluated on a real test site, accompanied by groundtruth annotation data of experts and revealed remarkable performance, exhibiting not only the feasibility of underground mapping but also the capacity to obtain exploitable results for underground robotic applications.
Autonomous subsurface mapping is a key characteristic of future robots to be realized in the construction domain, since it can be utilized in diverse applications of strategic importance. During the last years, the interest has been steered mainly towards the development of ground-penetrating radar (GPR) devices, rather than on the establishment of holistic subsurface reconstruction methods. To this end, the paper at hand introduces a simulation tool that comprises a) a surface operating rover and b) a sonar-based simulated GPR array capable, seamlessly integrated to build adjunct surface and subsurface maps. Specifically, by exploiting the onboard stereo camera of the robot and the GPR, mounted on a robotic-trailer topology, joint surface and subsurface mapping is performed. Further processing of the simulated GPR data is applied to detect and semantically annotate georeferenced buried utilities, while the localization of surface rover is also employed for the topographic correction of the accumulated Bscans during the robot's exploration. The proposed framework has been developed in the ROS framework and has been evaluated on the realistic simulation environment of Gazebo.
Autonomous surface exploration with mobile robots entails the problem of simultaneous trajectory planning and path tracking, while also on-board robot localization and kinematics control are essential. When it comes to articulated robotic setups, planning and control is more challenging since the towed component that typically bears a tool or a sensing mechanism should closely follow a specific trajectory in order to accurately complete its task. The paper at hand presents a holistic surface exploration method with an articulated mobile robot that tows a two-wheeled trailer. A variation of Boustrophedon global path planing module has been developed considering robot's embodiment for full field coverage, integrated with a real-time Model Predictive Controller (MPC) to ensure trailer's path following. Articulated robot's state estimation is provided with stereo-based visual odometry. The system has been evaluated both in simulation and in realistic environment, proving its ability to perform dense surface exploration that in our particular case, eventually enables a Ground Penetrating Radar (GPR) mounted on the trailer to autonomously scan the entire target area.
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