The Peopleremover—Removing Dynamic Objects From 3-D Point Cloud Data by Traversing a Voxel Occupancy Grid
| Autor: | Johannes Schauer, Andreas Nüchter |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Control and Optimization
Traverse Occupancy grid mapping Computer science Biomedical Engineering Point cloud 02 engineering and technology computer.software_genre 01 natural sciences Artificial Intelligence Voxel 0202 electrical engineering electronic engineering information engineering Computer vision business.industry Mechanical Engineering 010401 analytical chemistry 020207 software engineering 0104 chemical sciences Computer Science Applications Human-Computer Interaction Control and Systems Engineering Factory (object-oriented programming) Clutter Computer Vision and Pattern Recognition Artificial intelligence business computer Mobile mapping |
| Zdroj: | IEEE Robotics and Automation Letters. 3:1679-1686 |
| ISSN: | 2377-3774 |
| Popis: | Even though it would be desirable for most postprocessing purposes to obtain a point cloud without moving objects in it, it is often impractical or downright impossible to free a scene from all nonstatic clutter. Outdoor environments contain pedestrians, bicycles, and motor vehicles which cannot easily be stopped from entering the sensor range and indoor environments like factory production lines cannot be evacuated due to production losses during the time of the scan. In this letter, we present a solution to this problem that we call the “peopleremover.” Given a registered set of 3-D point clouds, we build a regular voxel occupancy grid and then traverse it along the lines of sight between the sensor and the measured points to find the differences in volumetric occupancy between the scans. Our approach works for scan slices from mobile mapping as well as for the more general scenario of terrestrial scan data. The result is a clean point cloud free of dynamic objects. |
| Databáze: | OpenAIRE |
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