1,743 research outputs found
Rapid 3D Modeling and Parts Recognition on Automotive Vehicles Using a Network of RGB-D Sensors for Robot Guidance
This paper presents an approach for the automatic detection and fast 3D profiling of lateral body panels of vehicles. The work introduces a method to integrate raw streams from depth sensors in the task of 3D profiling and reconstruction and a methodology for the extrinsic calibration of a network of Kinect sensors. This sensing framework is intended for rapidly providing a robot with enough spatial information to interact with automobile panels using various tools. When a vehicle is positioned inside the defined scanning area, a collection of reference parts on the bodywork are automatically recognized from a mosaic of color images collected by a network of Kinect sensors distributed around the vehicle and a global frame of reference is set up. Sections of the depth information on one side of the vehicle are then collected, aligned, and merged into a global RGB-D model. Finally, a 3D triangular mesh modelling the body panels of the vehicle is automatically built. The approach has applications in the intelligent transportation industry, automated vehicle inspection, quality control, automatic car wash systems, automotive production lines, and scan alignment and interpretation
Efficient 3D Mapping and Modelling of Indoor Scenes with the Microsoft HoloLens: A Survey
The Microsoft HoloLens is a head-worn mobile augmented reality device. It allows a real-time 3D mapping of its direct environment and a self-localisation within the acquired 3D data. Both aspects are essential for robustly augmenting the local environment around the user with virtual contents and for the robust interaction of the user with virtual objects. Although not primarily designed as an indoor mapping device, the Microsoft HoloLens has a high potential for an efficient and comfortable mapping of both room-scale and building-scale indoor environments. In this paper, we provide a survey on the capabilities of the Microsoft HoloLens (Version 1) for the efficient 3D mapping and modelling of indoor scenes. More specifically, we focus on its capabilities regarding the localisation (in terms of pose estimation) within indoor environments and the spatial mapping of indoor environments. While the Microsoft HoloLens can certainly not compete in providing highly accurate 3D data like laser scanners, we demonstrate that the acquired data provides sufficient accuracy for a subsequent standard rule-based reconstruction of a semantically enriched and topologically correct model of an indoor scene from the acquired data. Furthermore, we provide a discussion with respect to the robustness of standard handcrafted geometric features extracted from data acquired with the Microsoft HoloLens and typically used for a subsequent learning-based semantic segmentation
Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction
When conducting autonomous scanning for the online reconstruction of unknown
indoor environments, robots have to be competent at exploring scene structure
and reconstructing objects with high quality. Our key observation is that
different tasks demand specialized scanning properties of robots: rapid moving
speed and far vision for global exploration and slow moving speed and narrow
vision for local object reconstruction, which are referred as two different
scanning modes: explorer and reconstructor, respectively. When requiring
multiple robots to collaborate for efficient exploration and fine-grained
reconstruction, the questions on when to generate and how to assign those tasks
should be carefully answered. Therefore, we propose a novel asynchronous
collaborative autoscanning method with mode switching, which generates two
kinds of scanning tasks with associated scanning modes, i.e., exploration task
with explorer mode and reconstruction task with reconstructor mode, and assign
them to the robots to execute in an asynchronous collaborative manner to highly
boost the scanning efficiency and reconstruction quality. The task assignment
is optimized by solving a modified Multi-Depot Multiple Traveling Salesman
Problem (MDMTSP). Moreover, to further enhance the collaboration and increase
the efficiency, we propose a task-flow model that actives the task generation
and assignment process immediately when any of the robots finish all its tasks
with no need to wait for all other robots to complete the tasks assigned in the
previous iteration. Extensive experiments have been conducted to show the
importance of each key component of our method and the superiority over
previous methods in scanning efficiency and reconstruction quality.Comment: 13pages, 12 figures, Conference: SIGGRAPH Asia 202
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