Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites

Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules (e.g., context-awareness, control, localization, and mapping) on an embedded platform. Pixel-wise semantic segmentation provides a UV with the ability to be contextually aware of its surrounding environment. However, in the case of mobile robotic systems with limited computing resources, the large size of the segmentation model and high memory usage requires high computing resources, which a major challenge for mobile UVs (e.g., a small-scale vehicle with limited payload and space). To overcome this challenge, this paper presents a light and efficient deep neural network architecture to run on an embedded platform in real-time. The proposed model segments navigable space on an image sequence (i.e., a video stream), which is essential for an autonomous vehicle that is based on machine vision. The results demonstrate the performance efficiency of the proposed architecture compared to the existing models and suggest possible improvements that could make the model even more efficient, which is necessary for the future development of the autonomous robotics systems.Comment: The 2019 ASCE International Conference on Computing in Civil Engineerin

Vision-based Obstacle Removal System for Autonomous Ground Vehicles Using a Robotic Arm

Over the past few years, the use of camera-equipped robotic platforms for data collection and visually monitoring applications has exponentially grown. Cluttered construction sites with many objects (e.g., bricks, pipes, etc.) on the ground are challenging environments for a mobile unmanned ground vehicle (UGV) to navigate. To address this issue, this study presents a mobile UGV equipped with a stereo camera and a robotic arm that can remove obstacles along the UGV's path. To achieve this objective, the surrounding environment is captured by the stereo camera and obstacles are detected. The obstacle's relative location to the UGV is sent to the robotic arm module through Robot Operating System (ROS). Then, the robotic arm picks up and removes the obstacle. The proposed method will greatly enhance the degree of automation and the frequency of data collection for construction monitoring. The proposed system is validated through two case studies. The results successfully demonstrate the detection and removal of obstacles, serving as one of the enabling factors for developing an autonomous UGV with various construction operating applications.Comment: The 2019 ASCE International Conference on Computing in Civil Engineerin