METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS
Visual SLAM algorithms allow localizing the camera by mapping its environment by a point cloud based on visual cues. To obtain the camera locations in a metric coordinate system, the metric scale of the point cloud has to be known. This contribution describes a method to calculate the metric scale f...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-05-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/433/2018/isprs-archives-XLII-2-433-2018.pdf |
| _version_ | 1819086693150490624 |
|---|---|
| author | A. Hanel A. Mitschke A. Mitschke A. Mitschke R. Boerner D. Van Opdenbosch L. Hoegner D. Brodie U. Stilla |
| author_facet | A. Hanel A. Mitschke A. Mitschke A. Mitschke R. Boerner D. Van Opdenbosch L. Hoegner D. Brodie U. Stilla |
| author_sort | A. Hanel |
| collection | DOAJ |
| description | Visual SLAM algorithms allow localizing the camera by mapping its environment by a point cloud based on visual cues. To obtain the camera locations in a metric coordinate system, the metric scale of the point cloud has to be known. This contribution describes a method to calculate the metric scale for a point cloud of an indoor environment, like a parking garage, by fusing multiple individual scale values. The individual scale values are calculated from structures and objects with a-priori known metric extension, which can be identified in the unscaled point cloud. Extensions of building structures, like the driving lane or the room height, are derived from density peaks in the point distribution. The extension of objects, like traffic signs with a known metric size, are derived using projections of their detections in images onto the point cloud. The method is tested with synthetic image sequences of a drive with a front-looking mono camera through a virtual 3D model of a parking garage. It has been shown, that each individual scale value improves either the robustness of the fused scale value or reduces its error. The error of the fused scale is comparable to other recent works. |
| first_indexed | 2024-12-21T21:24:18Z |
| format | Article |
| id | doaj.art-0d92ffd6fbf24bcb9905d9bdc5ddeb8b |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-21T21:24:18Z |
| publishDate | 2018-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-0d92ffd6fbf24bcb9905d9bdc5ddeb8b2022-12-21T18:49:47ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-05-01XLII-243344010.5194/isprs-archives-XLII-2-433-2018METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMSA. Hanel0A. Mitschke1A. Mitschke2A. Mitschke3R. Boerner4D. Van Opdenbosch5L. Hoegner6D. Brodie7U. Stilla8Photogrammetry and Remote Sensing, Technical University of Munich, Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, Munich, GermanyESG Elektroniksystem- und Logistik-GmbH, Fuerstenfeldbruck, GermanyChair of Media Technology, Technical University of Munich, Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, Munich, GermanyChair of Media Technology, Technical University of Munich, Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, Munich, GermanyESG Elektroniksystem- und Logistik-GmbH, Fuerstenfeldbruck, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, Munich, GermanyVisual SLAM algorithms allow localizing the camera by mapping its environment by a point cloud based on visual cues. To obtain the camera locations in a metric coordinate system, the metric scale of the point cloud has to be known. This contribution describes a method to calculate the metric scale for a point cloud of an indoor environment, like a parking garage, by fusing multiple individual scale values. The individual scale values are calculated from structures and objects with a-priori known metric extension, which can be identified in the unscaled point cloud. Extensions of building structures, like the driving lane or the room height, are derived from density peaks in the point distribution. The extension of objects, like traffic signs with a known metric size, are derived using projections of their detections in images onto the point cloud. The method is tested with synthetic image sequences of a drive with a front-looking mono camera through a virtual 3D model of a parking garage. It has been shown, that each individual scale value improves either the robustness of the fused scale value or reduces its error. The error of the fused scale is comparable to other recent works.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/433/2018/isprs-archives-XLII-2-433-2018.pdf |
| spellingShingle | A. Hanel A. Mitschke A. Mitschke A. Mitschke R. Boerner D. Van Opdenbosch L. Hoegner D. Brodie U. Stilla METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS |
| title_full | METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS |
| title_fullStr | METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS |
| title_full_unstemmed | METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS |
| title_short | METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS |
| title_sort | metric scale calculation for visual mapping algorithms |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/433/2018/isprs-archives-XLII-2-433-2018.pdf |
| work_keys_str_mv | AT ahanel metricscalecalculationforvisualmappingalgorithms AT amitschke metricscalecalculationforvisualmappingalgorithms AT amitschke metricscalecalculationforvisualmappingalgorithms AT amitschke metricscalecalculationforvisualmappingalgorithms AT rboerner metricscalecalculationforvisualmappingalgorithms AT dvanopdenbosch metricscalecalculationforvisualmappingalgorithms AT lhoegner metricscalecalculationforvisualmappingalgorithms AT dbrodie metricscalecalculationforvisualmappingalgorithms AT ustilla metricscalecalculationforvisualmappingalgorithms |