Skyline locality sensitive hashing and simplified, efficient DEM rendering for large-scale, visual geo-localization
Abstract/Contents
- Abstract
- Locality sensitive hashing (LSH) of skyline features is used to expedite large-scale visual geo-localization in mountainous terrain. For queries with 60 degree FOV, LSH provides nearly the same localization accuracy as a state of the art reverse index method, but 10 times faster. LSH also provides approximately 25% better localization accuracy than a state of the art reverse index method pruned to give the same query speed. When tested on 196 photos from Baatz's CH1 data set LSH again matched or exceeded performance of the reverse index for queries with FOV > 30 degrees but was 40x faster than the full index, and was simultaneously 3x faster than an index pruned to contain less than 30% of the features. LSH and reverse index databases of skyline contourlets based on Baatz's method were built from rendered cubemaps of the ASTER GDEM. The coarse DEM resolution, small 2.5 degree feature width, and FOV-dependent database led to modest localization success rate of no more than 17% for any FOV bin, yet the relative accuracy of LSH vs. index-based methods clearly supports the claims of superior search speed with adjustable loss of accuracy compared to index pruning. Three open source tools are also presented, written in C++. Skybox Generator allows accurate, efficient rendering of images from large-scale DEMs, removing a significant barrier to entry for research in geometric matching-based localization at large scales. IM2SKY can extract skyline contourlet feature descriptors from sky-segmented images, and can quickly process large files. SKYLSH converts feature files into minhash signatures, hashes these signatures into LSH databases with adjustable numbers of files per band, and can perform LSH search for any features that are 32-bits or less.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Fetroe, Brandon Thayne |
|---|---|
| Degree supervisor | Kenny, Thomas William |
| Thesis advisor | Kenny, Thomas William |
| Thesis advisor | Rock, Stephen M |
| Thesis advisor | Schwager, Mac |
| Degree committee member | Rock, Stephen M |
| Degree committee member | Schwager, Mac |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Brandon Fetroe. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Brandon Thayne Fetroe
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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