Active Image-based Modeling with a Toy Drone

Image-based modeling techniques can now generate photo-realistic 3D models from images. But it is up to users to provide high quality images with good coverage and view overlap, which makes the data capturing process tedious and time consuming. We seek to automate data capturing for image-based modeling. The core of our system is an iterative linear method to solve the multi-view stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our fast MVS algorithm enables online model reconstruction and quality assessment to determine the NBVs on the fly. We test our system with a toy unmanned aerial vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that our system improves the efficiency of data acquisition and ensures the completeness of the final model.Comment: To be published on International Conference on Robotics and Automation 2018, Brisbane, Australia. Project Page: https://huangrui815.github.io/active-image-based-modeling/ The author's personal page: http://www.sfu.ca/~rha55

Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations

Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model of a scene is called Next Best View (NBV) planning. NBV planning approaches often use either volumetric (e.g., voxel grids) or surface (e.g., triangulated meshes) representations. Volumetric approaches generalise well between scenes as they do not depend on surface geometry but do not scale to high-resolution models of large scenes. Surface representations can obtain high-resolution models at any scale but often require tuning of unintuitive parameters or multiple survey stages. This paper presents a scene-model-free NBV planning approach with a density representation. The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries. This approach is shown experimentally to provide better surface coverage in lower computation time than the evaluated state-of-the-art volumetric approaches while moving equivalent distances

GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection

We study the problem of visually inspecting the surface of a bridge using an Unmanned Aerial Vehicle (UAV) for defects. We do not assume that the geometric model of the bridge is known. The UAV is equipped with a LiDAR and RGB sensor that is used to build a 3D semantic map of the environment. Our planner, termed GATSBI, plans in an online fashion a path that is targeted towards inspecting all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy grid map of the part of the environment seen by the UAV so far. We use semantic segmentation to segment the voxels into those that are part of the bridge and the surroundings. Inspecting a bridge voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As more parts of the environment are seen, we replan the path. We evaluate the performance of our algorithm through high-fidelity simulations conducted in Gazebo. We compare the performance of this algorithm with a frontier exploration algorithm. Our evaluation reveals that targeting the inspection to only the segmented bridge voxels and planning carefully using a GTSP solver leads to more efficient inspection than the baseline algorithms.Comment: 8 pages, 16 figure